We developed a practical framework to construct digital twins for predicting and optimizing triple-negative breast cancer (TNBC) response to neoadjuvant chemotherapy (NAC). This study employed 105 TNBC patients from the ARTEMIS trial (NCT02276443, registered on 10/21/2014) who received Adriamycin/Cytoxan (A/C)-Taxol (T). Digital twins were established by calibrating a biology-based mathematical model to patient-specific MRI data, which accurately predicted pathological complete response (pCR) with an AUC of 0.82. We then used each patient's twin to theoretically optimize outcome by identifying their optimal A/C-T schedule from 128 options. The patient-specifically optimized treatment yielded a significant improvement in pCR rate of 20.95-24.76%. Retrospective validation was conducted by virtually treating the twins with AC-T schedules from historical trials and obtaining identical observations on outcomes: bi-weekly A/C-T outperforms tri-weekly A/C-T, and weekly/bi-weekly T outperforms tri-weekly T. This proof-of-principle study demonstrates that our digital twin framework provides a practical methodology to identify patient-specific TNBC treatment schedules.

Background and Purpose: Inflammatory breast cancer (IBC) is a rare and aggressive disease, accounting for 2-4% of all breast cancers. One third of the patients have distant metastases at initial presentation [1]. Trimodality therapy beginning with neoadjuvant systemic therapy (NAST) is associated with the best local control and survival (2). The ability to determine tumor response early in the NAST course is essential in optimizing treatment and predicting prognosis (2,3). MRI (Magnetic Resonance Imaging) is suggested to be the best modality for assessing response. Cellular changes such as cell membrane destruction or tumor lysis lead to increased water diffusivity and can be measured on MRI (4). Apparent diffusion coefficient (ADC) is a measurement of the change in water diffusivity, calculated from the diffusion weighted imaging (DWI) scans. In this study, we investigated quantitative DWI for differentiating pathological complete response (pCR) from non-pCR in IBC patients after NAST. Methods and Materials: All patients were selected from an IRB-approved prospective IBC registry at one tertiary academic center and treated between December 2019 to November 2022. A retrospective chart review was performed to identify IBC patients with analyzable imaging and pathology records. Patients with available pre-treatment and mid-treatment MRI with DWI scans were included. Of 262 scans, 149 pre-treatment MRI, 13 outside films (OSF) without DWI, and 5 biopsy MRIs were excluded from analysis. Of the remaining 97, 27 had both pre-treatment and mid-treatment DWI with same b-values (diffusion coefficient value applied to images) available for evaluation. All MRI exams were performed on 3T scanners (either GE Healthcare or Siemens Healthineers) using phased-array dedicated breast coils. Typical scan parameters for DCE scans were: flip angle = 12°, TR/TE ∼6/2 msec, acquisition matrix = 320 x 320 x 112, FOV = 30 x 30 x 18 cm, and temporal resolution = 10 secs. Contrast agent was administered intravenously via an MR-compatible injector and flushed with 20 mL saline solution. DWI was acquired with two b-values of 100, 800 sec/mm2 and scan parameters as follows: TR/TE = 4000/70 msec, and FOV = 16 x 16 x 6.4 cm. Quantitative ADC maps were computed using a mono-exponential model. Image analysis was performed on a DynaCAD system (Invivo Corporation, Pewaukee, WI). Three regions-of-interest (ROIs) of the index breast tumor, axillary node, and thickened skin were segmented on DCE by an experienced breast radiologist (24 years). ROIs were transferred to the ADC maps and co-registered with DCE images. The quantitative ROI histogram values (median, mean, standard deviation, minimum, maximum, skewness, and kurtosis) were extracted. Statistical analyses were performed to determine the association between these measurement and the patients’ pCR status and PFS. Results: Median follow up was 14.5 months. 19 patients had non-pCR and 8 had pCR. At the time of this analysis, 20 patients were alive and 7 deceased. The median and mean ADC values of tumor was 1.326 and 1.333, respectively. Both median and mean ADC values were correlated to pCR, with p-value of 0.0074 and 0.0071, respectively. None of the remaining values associated with DWI scans of the index tumor, axillary node, and skin ROIs (standard deviation, minimum, maximum, skewness, and kurtosis) was significantly correlated to pCR. No statistically significant correlation was demonstrated between ROI measured and PFS (correlation coefficients = -0.35 to 0.48). Conclusion: DWI is sensitive to changes in water cellularity and has the potential to characterize early tumoral response during NAST. Histogram analyses of ADC in IBC reflects tumor heterogeneity. Our pilot study shows promise of the median and mean ADC values as imaging biomarkers for predicting pCR at mid-NAST in IBC patients. Citation Format: Huong Le-Petross, Jong Bum Son, Jingfei Ma, Megumi Kai, Gary Whitman, Mary Guirguis, Miral Patel, Megha Kapoor, Susie Sun, Bora Lim, Angela Alexander, Vincent Valero, Anthony Lucci, Wendy Woodward. Quantitative Diffusion Weighted Imaging for Predicting Response to Neoadjuvant Therapy in Patients with Inflammatory Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-05-23.

Purpose: To develop deep learning models for predicting the pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in patients with triple-negative breast cancer (TNBC) based on pretreatment multiparametric breast MRI and clinicopathological data. Methods: The prospective institutional review board-approved study [NCT02276443] included 282 patients with stage I–III TNBC who had multiparametric breast MRI at baseline and underwent NAST and surgery during 2016–2021. Dynamic contrast-enhanced MRI (DCE), diffusion-weighted imaging (DWI), and clinicopathological data were used for the model development and internal testing. Data from the I-SPY 2 trial (2010–2016) were used for external testing. Four variables with a potential impact on model performance were systematically investigated: 3D model frameworks, tumor volume preprocessing, tumor ROI selection, and data inputs. Results: Forty-eight models with different variable combinations were investigated. The best-performing model in the internal testing dataset used DCE, DWI, and clinicopathological data with the originally contoured tumor volume, the tight bounding box of the tumor mask, and ResNeXt50, and achieved an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.60–0.88). The best-performing models in the external testing dataset achieved an AUC of 0.72 (95% CI: 0.57–0.84) using only DCE images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNeXt50) and an AUC of 0.72 (95% CI: 0.56–0.86) using only DWI images (originally contoured tumor volume, enlarged bounding box of tumor mask, and ResNet18). Conclusions: We developed 3D deep learning models based on pretreatment data that could predict pCR to NAST in TNBC patients.

Purpose To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN predictions of the calibrated model parameters were used to estimate tumor response at the end of NAC. CNN performance in the estimations of total tumor volume (TTV), total tumor cellularity (TTC), and tumor status was evaluated. Model-predicted TTC and TTV measurements were compared with MRI-based measurements using the concordance correlation coefficient and area under the receiver operating characteristic curve (for predicting pathologic complete response at the end of NAC). Results The study included 118 female patients (median age, 51 years [range, 29-78 years]). For comparison of CNN predicted to measured change in TTC and TTV over the course of NAC, the concordance correlation coefficient values were 0.95 (95% CI: 0.90, 0.98) and 0.94 (95% CI: 0.87, 0.97), respectively. CNN-predicted TTC and TTV had an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.34, 0.94) and 0.72 (95% CI: 0.40, 0.95) for predicting tumor status at the time of surgery, respectively. Conclusion Deep learning integrated with a biology-based mathematical model showed good performance in predicting the spatial and temporal evolution of a patient's tumor during NAC using only pre-NAC MRI data. Keywords: Triple-Negative Breast Cancer, Neoadjuvant Chemotherapy, Convolutional Neural Network, Biology-based Mathematical Model Supplemental material is available for this article. Clinical trial registration no. NCT02276443 ©RSNA, 2024 See also commentary by Mei and Huang in this issue.

Introduction: Neoadjuvant therapy is standard of care for locally advanced triple-negative breast cancer (TNBC), yet only 50-65% achieve a pathological complete response (pCR). Personalizing therapeutic regimens remains a challenge. We have developed MRI-guided digital twins (mathematical models predicting patient responses to neoadjuvant chemotherapy (NAC)) to address this need [1,2]. This study aims to validate digital twins by virtually replicating results from previous clinical trials investigating the efficacy of various chemotherapy regimens. Methods: This study employed 105 TNBC patients from the ARTEMIS trial (NCT02276433) [3] who received 4 cycles (4×) of Adriamycin/Cytoxan (A/C) every 2-3 weeks, followed by 12 cycles (12×) of weekly Taxol (T). Each patient had multi-parametric MRI to monitor tumor anatomy, perfusion, and cellularity. Personalized digital twins were created by calibrating a biology-based mathematical model to each patient’s MRI data collected before, after 2 cycles, and at the end of A/C [1]. The calibrated digital twin has been shown to predict the response of patient’s tumor to both the actual and alternative NAC regimens [2]. The accuracy of predicted response to the actual regimen was validated in prior work [1]. To further validate the accuracy of predicted response to alternative NAC regimens, we simulated the response of the individual patients in our cohort to the regimens investigated in three landmark clinical trials (INT C9741 [4], ECOG 1199 [5-6], and SWOG S0221 [7]) that compared A/C and T administrative schedules in locally advanced breast cancer, and determine if our digital twin-based methodology can recapitulate the trial observations. INT C9741 compared two A/C-T regimens: 1) tri-weekly A/C-T: 4× A/C per 3 weeks → 4× T per 3 weeks, and 2) dose-dense A/C-T: 4× A/C per 2 weeks → 4× T per 2 weeks. The trial found dose-dense A/C-T significantly improved the disease-free and overall survival (DFS/OS) compared to tri-weekly A/C-T. ECOG 1199, along with recent meta-analyses [8], compared three T regimens combined with tri-weekly A/C: 1) tri-weekly Taxol: 4× A/C per 3 weeks → 4× T per 3 weeks, 2) bi-weekly Taxol: 4× A/C per 3 weeks → 4× T per 2 weeks, and 3) weekly Taxol: 4× A/C per 3 weeks → 12× T weekly. Weekly and bi-weekly Taxol provided similar DFS/OS superior to tri-weekly Taxol, especially for TNBC [6]. SWOG S0221 compared four different A/C-T regimens: 1) Arm 1: 6× A/C per 2 weeks → 6× T per 2 weeks, 2) Arm 2: 15× A/C weekly → 6× T per 2 weeks, 3) Arm 3: 6× A/C per 2 weeks → 12× T weekly, 4) Arm 4: 15× A/C weekly → 12× T weekly. All regimens showed similar DFS over the population, with a non-significant DFS/OS improvement in TNBC patients with bi-weekly A/C-T (Arm 1). Results: For INT C9741, our digital twin predictions yielded a pCR rate of 49.52% and 73.33% for tri-weekly and dose-dense A/C-T, respectively, with a significant difference (P < 0.001) via χ2 test. This result was consistent with the trial observation on dose-dense A/C-T. For ECOG 1199, our digital twin predictions yielded pCR rates of 49.52%, 60.00%, and 55.24%, respectively, for the tri-weekly, bi-weekly, and weekly T regimens, with no significant difference (P > 0.1). The weekly and bi-weekly T led to higher pCR rates than the tri-week T, consistent with the trial observations. For SWOG S0221, our digital twin predictions yielded pCR rates of 79.05%, 72.38%, 73.33%, and 69.52% for the four regimens, with no significant difference. Arm 1 had the highest pCR rate, again consistent with the trial outcome. Conclusion: Our digital twin predictions matched previous clinical trial observations on various NAC regimens, supporting their use in tailoring NAC for TNBC patients. This method can also be beneficially used for designing adaptive clinical trials. [1] Wu et al., Cancer Res, 2022. [2] Wu et al., SABCS, 2023. [3] Yam et al., Clin Cancer Res, 2021. [4] Citron et al., J Clin Oncol, 2003. [5] Sparano et al., NEJM, 2008. [6] Sparano et al., J Clin Oncol, 2015. [7] Budd et al., J Clin Oncol, 2015. [8] Khan et al., J Clin Oncol, 2020. Citation Format: Chengyue Wu, Ernesto A.B.F. Lima, Casey E. Stowers, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Retrospective validation of digital twin-based prediction of personalized triple negative breast cancer response to neoadjuvant therapy regimens [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P2-11-13.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with variable response to neoadjuvant systemic therapy (NAST). Patients with pathologic complete response (pCR) following NAST have improved survival. Our goal was to establish readily accessible imaging biomarker to identify which TNBC patients will have pCR. Building on prior favorable results in the literature, we hypothesized that manually measured tumor volume changes at DCE-MRI may predict pCR early during NAST. This prospective study included 287 stage I-III TNBC patients who underwent DCE-MRI at baseline, after two and four cycles of NAST, with pCR accessed at surgery (NCT02276443). Tumor volume and percentage tumor volume reduction were calculated, and their correlation with pCR was evaluated. Our study showed that manually extracted tumor volume changes from DCE-MRI early during NAST were able to predict pCR with high accuracy and can serve as a clinically relevant imaging biomarker for prediction of NAST response in TNBC patients.

PURPOSE:To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI). METHODS:The study included 50 patients with rectal cancer who underwent rectal MRI consecutively between July 7, 2020 and January 20, 2021 using a T2W 3D FSE sequence with DLR and conventional reconstruction. Three radiologists reviewed the two sets of images, scoring overall SNR, motion artifacts, and overall image quality on a 3-point scale and indicating clinical preference for DLR or conventional reconstruction based on those three criteria as well as image characterization of bowel wall layer definition, tumor invasion of muscularis propria, residual disease, fibrosis, nodal margin, and extramural venous invasion. RESULTS:Image quality was rated as moderate or good for both DLR and conventional reconstruction for most cases. DLR was preferred over conventional reconstruction in all of the categories except for bowel wall layer definition. CONCLUSION:Both conventional reconstruction and DLR provide acceptable image quality for T2W 3D FSE imaging of rectal cancer. DLR was clinically preferred over conventional reconstruction in almost all categories.

Abstract Introduction Neoadjuvant chemotherapy (NAC) is the standard-of-care (SOC) for patients with locally advanced triple-negative breast cancer (TNBC) [1]. Unfortunately, only about half of TNBC patients achieve a pathological complete response (pCR) at the completion of NAC [2]. With the recent FDA approval of immunotherapy, the response rate improved by about 8% [3]. To try to further improve NAC response rates, we build upon methods that predict treatment response to optimize interventions and outcomes. Our previous studies have demonstrated that excellent predictive accuracy of TNBC response to NAC was achieved through calibrating mechanism-based models to both pre- and on- treatment imaging data [4]. However, by requiring on-treatment imaging data, therapy optimization can only occur after a patient begins NAC. Here, our goal is to relax the requirement of on-treatment imaging data such that we can predict, and potentially optimize, a patient’s response to NAC before initiating it. We will accomplish this through combining data-driven approaches with mechanism-based modeling to obtain spatiotemporal predictions of tumor response prior to NAC. More specifically, we integrate a reaction-diffusion equation based mathematical model with a U-Net based convolutional neural network (CNN). Methods The reaction-diffusion equation models the development of tumor cellularity over time as the sum of tumor cell diffusion, controlled by a fixed diffusivity, and tumor cell proliferation, controlled by a calibrated net proliferation rate. The net proliferation rate implicitly accounts for both proliferative and drug induced death effects. We calibrated the net proliferation rates in this model on a patient-specific basis for 128 patients from the ARTEMIS trial (NCT02276443) using three imaging timepoints – pre- (V1), post two cycles (V2), and post four cycles (V3) of A/C. We then employed a CNN to characterize the relationship between the calibrated net proliferation rates and the pre-treatment imaging data. The CNN takes pretreatment imaging inputs of the apparent diffusion coefficient map, percent enhancement maps from the dynamic contrast enhanced MRI timecourse, and precontrast T1 map. The CNN outputs an estimate of the calibrated net proliferation rate which can be used to run the mathematical model forward in time to predict cellularity maps at V2/3. In training the CNN, our loss considers both the concordance correlation coefficient (CCC) and the normalized root mean square error (NRMSE). We calculated loss on the net proliferation rate predictions as NRMSE with a CCC penalty to aid in characterizing the non-Gaussian distribution of the net proliferation rate. We calculated loss on the cellularity predictions as the CCC between predicted and measured voxel-wise cellularity. The CNN was trained using the Adam optimizer and a fivefold cross validation with a withheld test set of 26 patients. Results Using the CNN predictions to forward run the mechanism-based model in time to V3 we calculated change in total tumor cellularity and volume from V1 to V3. In the test cohort, we obtained CCC values between the predicted and measured changes of 0.95 and 0.91 for cellularity and volume, respectively. Discussion and summary Through integrating mechanistic modeling and deep learning, we can accurately predict the spatiotemporal development of TNBC response to NAC using only pretreatment imaging data. We will build upon these results by expanding our model to include separate proliferation and drug induced death terms to allow for therapy intervention and optimization. Additionally, we will incorporate other pretreatment data types including genetic data. [1] Liedtke et al., J of Clin Oncol, 2008. [2] Spring et al., Clin Cancer Res, 2020. [3] Schmid et al., NEJM, 2020. [4] Wu et al., Cancer Res, 2022. Citation Format: Casey Stowers, Chengyue Wu, Sidharth Kumar, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan Tamir, Gaiane Rauch, Thomas Yankeelov. Integrating mechanism-based and data-driven modeling to predict the response of triple negative breast cancer to therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-03.

Background Assessment of treatment response in triple‐negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). Purpose To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. Study Type Prospective. Population Eighty‐six women (average age: 51 ± 11 years) with biopsy‐proven clinical stage I–III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). Field Strength/Sequence 3.0 T/reduced field of view single‐shot echo‐planar DTI sequence. Assessment Three MRI scans were acquired longitudinally (pre‐treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. Statistical Tests Longitudinal changes of DTI features were tested for discrimination of pCR using Mann–Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. Results 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non‐pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non‐pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. −0.017 ± 0.042 mm 2 /s). Data Conclusion Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. Evidence Level 1 Technical Efficacy Stage 4

Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.

Abstract Purpose: Neoadjuvant immunotherapy (NIT) in combination with neoadjuvant chemotherapy (NCT) was recently approved for treatment of TNBC patients with increased rates of pathologic complete response (pCR) compared to NCT alone. The aim of this study was to evaluate if dynamic contrast-enhanced (DCE)-MRI performed after 2 and/or 4 cycles of NIT + NCT, can predict which patients will achieve pCR, potentially triaging them to continuation of NIT+NCT or, when appropriate, to de-escalation trials. Alternatively, identified chemoresistant tumors who are unlikely to achieve pCR may be directed to other treatment strategies, including novel targeted trials, and avoid the unnecessary toxicity of NIT. Methods and Materials: Preliminary analysis included 64 patients from prospective IRB-approved study (NCT02276443) with stage I-III TNBC who underwent DCE-MRI at baseline (BL), after 2 cycles (C2), and 4 cycles (C4) of NIT combined with standard of care NCT (Paclitaxel +/- carboplatin). Tumor volumes were calculated using 3 axis measurements of the index lesion at the DCE MRI and percent tumor volume reduction (TVR) between BL, C2, and C4 was calculated. pCR was assessed at surgery after completion of neoadjuvant treatment. Correlation between pCR and TVR was evaluated using ROC analysis. Results: 59% (38/64) of TNBC patients achieved pCR after NIT+NCT. DCE-MRI after 2 cycles of NIT+NCT was able to predict pCR with an AUC of 0.71 (95% CI: 0.57-0.84). TVR >90% at C2 predicted pCR with PPV 86%, and TVR < 35% predicted chemoresistance with NPV 100%. Following 4 cycles of treatment DCE-MRI was able to predict pCR with an AUC of 0.81 (95% CI: 0.69-0.92). TVR >95% at C4 was predictive of chemosensitivity with PPV 82%, while TVR < 75% was predictive of chemoresistance with NPV 100%. Conclusions: DCE-MRI volumetric changes early during NIT + NCT were able to predict pCR status of TNBC patients as either excellent responders or nonresponders, triaging them to SOC neoadjuvant therapy with option for de-escalation trials, or targeted therapies, respectively. These preliminary results will be validated in the larger cohort after completion of the ongoing prospective clinical trial. Citation Format: Gaiane Rauch, Mary Guirguis, Miral Patel, Rosalind Candelaria, Rania Mohamed, Tanya Moseley, H. T. Carisa Le-Petross, Jessica Leung, Gary Whitman, Deanna Lane, Marion Scoggins, Frances Perez, Jia Sun, Sanaz Pashapoor, Zhan Xu, Jason White, Peng Wei, Brandy Reed, Jong Bum Son, Ken-Pin Hwang, Bikash Panthi, Anil Korkut, Lei Huo, Kelly Hunt, Alyson Clayborn, Jennifer Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Wei Yang, Jingfei Ma, Beatriz Adrada. Early prediction of response to Neoadjuvant Immunotherapy in Triple Negative Breast Cancer (TNBC) with DCE-MRI [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS05-07.

Abstract Introduction: Neoadjuvant systemic therapy (NAT) has been the standard-of-care of stage II-III, locally advanced triple-negative breast cancer (TNBC). However, about 50% of TNBC patients achieve a pathological complete response (pCR) to conventional neoadjuvant chemotherapy (NAC)1. Recently approved combination of NAC with the immunotherapy pembrolizumab, has improved the pCR rate by 7.5%2, although with a 44% risk of immune-related adverse reaction3. Aside from the need to develop new therapies with higher efficacy and lower toxicity, a critical barrier to improving TNBC response is the lack of rigorous ways to personally tailor therapeutic regimens. We seek to address this challenge by employing digital twins (i.e., mathematical models that provide virtual representation of individual patients and predict the changes at future time points) to systematically evaluate individual TNBC patient’s response to different NAT regimens, thereby patient-specifically optimizing treatments. Methods: A TNBC cohort (n = 139) from the ARTEMIS trial (NCT02276433)4 was used for this study. All patients received 4 cycles of Adriamycin/Cytoxan (A/C) every 2 weeks, followed by 12 cycles of weekly Taxol or experimental therapy in Phase II trials. All patients had surgery after NAT and post-surgical pathology to assess response status. For each patient, longitudinal MRIs were collected before, during, and after A/C. We have developed digital twins to integrate the longitudinal MRIs with a mechanism-based model to accurately predict TNBC response5. The model was based on a reaction-diffusion equation that describes the change in tumor cellularity due to migration, proliferation, and drug-induced death. With parameters personalized using MRIs, the patient-specific model (i.e., digital twin) significantly improved the accuracy to predict pCR5. We investigated patient-specific treatment optimization on 37 (19 pCR, 18 non-pCR) chemo-sensitive patients (≥ 70% volume decrease after A/C) who received only NAC. We evaluated the effect of altering the A/C/Taxol schedules on patient response. Specifically, each patient’s digital twin was used to predict the patient’s response to 128 clinically reasonable schedules of A/C/Taxol; i.e., 8 candidate A/C schedules combining with 16 candidate Taxol schedules (Table 1). The predicted response (pCR or non-pCR) from each alternative schedule was compared to the patient response from the actual treatment. Results: Without changing the total dose, shortening the duration of A/C/Taxol administration increased the treatment efficacy. The effectiveness of altering the schedules varied substantially in different patients. In particular, 8 patients who had non-pCR responses to their actual treatment were predicted to achieve pCR with the dense-dose Taxol (i.e., 4 cycles Taxol, 2 weeks per cycle), indicating a 21.62% improvement of pCR rate in the cohort. Discussion and conclusion: The preliminary results with our digital twin approach provided a unique opportunity of improving TNBC response to NAT through patient-specific optimization of therapeutic schedules. The ongoing effort focuses on accounting for toxicity and investigating the effects of altering therapy types and doses in combination with the schedules on the patient response. [1] Spring et al., Clin Cancer Res, 2020. [2] Schmid et al., NEJM, 2022. [3] Shah et al., Clin Cancer Res, 2022. [4] Yam et al., Clin Cancer Res, 2021. [5] Wu et al., Cancer Res, 2022. Table 1. Candidate therapeutic schedules Citation Format: Chengyue Wu, Ernesto Lima, Casey Stowers, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane Rauch, Thomas Yankeelov. Optimizing therapeutic regimens via digital twins to improve triple negative breast cancer response to neoadjuvant therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-03-06.

Objectives Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. Methods Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality. The generalized estimating equation method was used to assess the effect of methods on the Likert scales. Quantitatively, the contrast-to-noise ratio and signal-to-noise ratio (SNR) of the iliac muscle were calculated, and pairwise comparisons were performed based on a linear mixed model. P values were adjusted using the Dunnett method. Interobserver agreement was assessed using the κ statistic. P value was considered statistically significant at less than 0.05. Results Qualitatively, DL 50 and DL 75 were ranked as the best sequences in 86% of cases. Images generated by the DL method were significantly better than non-DL images (P < 0.0001). Iliacus muscle SNR on DL 50 and DL 75 was significantly better than non-DL images (P < 0.0001). There was no difference in contrast-to-noise ratio between the DL and non-DL techniques in the iliac muscle. There was a high percent agreement (97.1%) in terms of DL sequences' superior image quality (97.1%) and sharpness (100%) relative to non-DL images. Conclusion The utilization of DL reconstruction improves the image quality of PROPELLER sequences with improved SNR quantitatively.

PURPOSE Triple-negative breast cancer (TNBC) is a heterogeneous disease with variable response to neoadjuvant therapy (NAT). Pathologic complete response (pCR) has become a prognostic marker for overall and disease-free survival. The aim of this study was to determine if dynamic contrast-enhanced (DCE)-MRI after 2 and/or 4 cycles of NAT can identify patients with a high likelihood of achieving pCR, triaging them to standard of care (SOC), or, when appropriate, to de-escalation trials. Conversely, we aimed to identify chemoresistant tumors that are unlikely to achieve pCR and may benefit from escalated targeted trials. METHOD AND MATERIALS 309 patients with stage I-III TNBC underwent DCE-MRI (temporal resolution: 9-12 sec) at baseline (BL), 2 cycles (C2), and 4 cycles (C4) of SOC doxorubicin/cyclophosphamide (AC) NAT as part of a prospective IRB-approved study (NCT02276443). Tumor volumes of the index lesion were calculated using 3 axis measurements during the early phase of the DCE-MRI (60s). Percent tumor volume reduction (TVR) between BL, C2, and C4 was calculated. Patients were randomly assigned to a training or a validation cohort in a 1:1 ratio. pCR was assessed at surgery after completion of SOC NAT. Correlation between pCR and TVR was evaluated using ROC analysis. RESULTS Of 309 TNBC patients, 136 (44%) achieved pCR. Following 2 cycles of NAT, TVR >80% was predictive of pCR (chemosensitivity), while TVR ≤ 55% was predictive of non-pCR (chemoresistance) with PPV 80%, NPV 89%, AUC 0.811 (0.73~0.893, p< 0.0001) in the training cohort, and PPV 82%, NPV 85%, AUC 0.815 (CI:0.736~0.894, p< 0.0001) in the validation cohort. Following 4 cycles of NAT, TVR >90% was predictive of pCR, while TVR ≤80% was predictive of non-pCR with PPV 80%, NPV 84%, AUC 0.827 (0.756~0.898, p< 0.0001) in the training cohort and with PPV 73%, NPV 82%, AUC 0.785 (CI:0.709~0.862, p< 0.001) in the validation cohort. Using this model, the pCR status was correctly classified in 50% of TNBC patients using C2 DCE-MRI in the training cohort, and 54% in the validation cohort. Only 8% were misclassified in the training cohort, and 10% in the validation cohort. Using C4 DCE-MRI, the pCR status of 61% and 57% of TNBC was correctly classified in the validation and the testing cohorts, respectively. 12% were misclassified in the validation cohort, and 21% in the testing cohort. CONCLUSION DCE-MRI after 2 and 4 cycles of AC-based NAT correctly predicted the pCR status of 54% and 57% of TNBC patients, respectively, as either excellent responders or nonresponders with high AUC 0.811 and 0.827. This may allow patients to be triaged to SOC NAT with option of de-escalation or early targeted therapies for non-responders. Citation Format: Mary S. Guirguis, Beatriz Adrada, Miral Patel, Frances Perez, Rosalind Candelaria, Wei Yang, Jia Sun, Rania M. Mohamed, Medine Boge, H. T. Carisa Le-Petross, Jessica Leung, Gary J. Whitman, Deanna L. Lane, Marion E. Scoggins, Tanya Moseley, Benjamin Musall, Jason White, Sanaz Pashapoor, Peng Wei, Jong Bum Son, Ken-Pin Hwang, Bikash Panthi, Mark Pagel, Lei Huo, Kelly K. Hunt, Elizabeth Ravenberg, Alastair M. Thompson, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Stacy Moulder, Clinton Yam, Jingfei Ma, Gaiane Rauch. DCE-MRI for early prediction of excellent response versus chemoresistance in triple negative breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-15.

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.

More than 50% of triple negative breast cancer (TNBC) patients do not respond well to the standard-of-care neoadjuvant therapy (NAT). Therefore, methods capable of predicting treatment response will be highly useful to optimize intervention and outcomes for TNBC patients. To address this problem, we aim to integrate quantitative magnetic resonance imaging (MRI) with biology-based mathematical modeling and deep learning to make patient-specific predictions of TNBC response to NAT using only pretreatment data. TNBC patients (n = 150) enrolled in the ARTEMIS trial (NCT02276443) received doxorubicin/cyclophosphamide (A/C) followed by paclitaxel. MRI exams were acquired for each patient at the following timepoints: (1) before initiation of NAT, (2) after two A/C cycles, (3) after four A/C cycles, and (4) at the conclusion of NAT. Using patient-specific MRI data from the first two exams, we calibrated a biology-based mathematical model to characterize migration, proliferation, and treatment-induced death of tumor cells. We then used this model as a digital twin to predict spatiotemporal tumor response. While effective, this approach requires the patient to have completed at least part of their NAT regime before we are able to predict therapeutic response. To relax this requirement, we have developed an approach that combines deep learning and biology-based mathematical modeling to predict the response of TNBC to NAT before treatment initiation. Specifically, we integrated a U-Net-based convolutional neural network with our mathematical model to regress between pre-treatment data and the model parameters obtained from a training set. Using parameters from learning a network with a subset of 68 patients, our mathematical model yielded concordance correlation coefficients between the predicted and measured patient-specific changes in tumor cellularity and volume at the third imaging point of 0.95 and 0.92, respectively. Spatially, we obtain the median difference between predicted and measured percent change in cellularity from visit one to visit three for each patient, giving a mean (95% confidence interval) of -6.51% (-7.13%, -5.90%) across all patients. These encouraging results may be further improved using methods such as expanding to a spatially-resolved proliferation rate, including genetic and/or histological data, and extending the deep learning framework to the end of the treatment course to predict pathological response. This approach allows us to obtain patient-specific predictions of response before NAT commences, thereby providing the opportunity to optimize interventions and patient outcomes. Citation Format: Casey E. Stowers, Chengyue Wu, Sidharth Kumar, Ernesto A.B.F. Lima, Xhan Zu, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I. Tamir, Gaiane M. Rauch, Thomas E. Yankeelov. Developing MRI-based digital-twins via mathematical modeling and deep learning to predict the response of triple-negative breast cancer to neoadjuvant therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 845.

Background and Purpose Early prediction of neoadjuvant systemic therapy (NAST) response in triple negative breast cancer (TNBC) patients could potentially aid in the selection of alternative therapies and avoid unnecessary toxicity in patients unlikely to achieve pathologic complete response (pCR) with NAST. In this study, we investigated the radiomic features of the peritumoral and the tumoral regions from dynamic contrast enhanced (DCE) MRI acquired at different time points of NAST for early treatment response prediction in TNBC. Methods and Materials This study included 182 biopsy-confirmed stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433). All patients underwent DCE-MRI on a GE 3T MRI scanner at baseline (BL), after two (C2) and four (C4) cycles of doxorubicin/cyclophosphamide based chemotherapy and before surgery. The peritumoral and the tumoral regions were segmented manually by two fellowship-trained radiologists using early phase (2.5 min) DCE-MRI subtraction images. Ten first order radiomic features, 300 grey-level-co-occurrence matrix (GLCM) features along with their absolute and relative differences (C4/BL, C2/BL, C4/C2) between the 3 imaging time points were extracted from the peritumoral and the tumoral regions. Patients were randomly divided into training and testing sets in a 2:1 ratio. For univariate analysis, area under the receiver operating characteristics curve (AUC ROC) was measured to determine the features most predictive of pCR/non-pCR. Wilcoxon Rank Sum test was used to test the statistical significance of predictive performance. In multivariate analysis, radiomic models were established using logistic regression with elastic net regularization followed by 5-fold cross validation for performance assessment. Results Eighty-eight (48%) patients had pCR (59 training, 29 testing) and 94 (52%) patients had non-pCR (63 training, 31 testing). Twenty-five radiomic features (4 from peritumoral C4, 5 from tumoral C4, 4 from peritumoral C4/BL, 6 from tumoral C4/BL, 2 from peritumoral C4/C2 and 4 from tumoral C4/C2) were statistically significant with AUC ≥ 0.75 in both the training and the testing sets at the univariate analysis. The significant features at C4 had AUCs of 0.75-0.79 for the training set and 0.76-0.81 for the testing set. Changes measured between C4 and BL or C2 showed AUC of 0.76-0.84 in the training and 0.75-0.81 in the testing datasets. Eleven multivariate regression models comprised of radiomic features at BL, C2, C4 and their changes (C4/BL, C4/C2 and C2/BL) showed an AUC of 0.80-0.84 for cross validation and an AUC of 0.80-0.82 for independent testing. Conclusions Radiomic models using longitudinal DCE MRI parameters of peritumoral and tumoral regions during NAST have the potential to predict pCR in TNBC patients undergoing NAST. Citation Format: Bikash Panthi, Rania M. Mohamed, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Elizabeth Ravenberg, Alyson Clayborn, Mark Pagel, Huiqin Chen, Jia Sun, Peng Wei, Alastair M. Thompson, Stacy Moulder, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Jingfei Ma, Gaiane Rauch. Longitudinal DCE-MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy (NAST) in Triple Negative Breast Cancer (TNBC) Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-34.