Background and purpose:Deep inspiration breath hold (DIBH) is a respiratory control technique designed to minimize cardiopulmonary toxicity induced by adjuvant radiotherapy for left-sided breast cancer. This study aimed to evaluate the dosimetric effects of DIBH compared with those of free breathing (FB) in patients undergoing postoperative radiotherapy for left-sided breast cancer in order to assess differences in potential cardiopulmonary complications between the two approaches. Methods:Forty patients with left-sided breast cancer undergoing postoperative adjuvant radiotherapy were included. Of these, 20 patients received conventional radiotherapy (5000 cGy in 25 fractions), and the other 20 patients received hypofractionated radiotherapy (4005 cGy in 15 fractions, with a sequential boost to the tumor bed of 1000 cGy in 5 fractions). Treatment plans were performed on both FB and DIBH computed tomography images for each patient. Mean heart dose (MHD), mean left lung dose, and other cardiopulmonary parameters were recorded. The normal tissue complication probability (NTCP) model was applied to predict complications, including pericarditis/pericardial effusion, heart valvular dysfunction (RVD), coronary artery disease (CAD), and Radiation Therapy Oncology Group (RTOG) grade ≥ 2 radiation pneumonitis (RP). Clinical anatomical features, including the number of contact layers between the heart surface and the chest wall (defined as Contact_Heart) and the Haller index (HI), were also documented, followed by correlation analysis. Results:The DIBH technique significantly reduced MHD by 22% in both conventional radiotherapy (P = 0.004, 95% confidence interval [CI][14, 31]) and hypofractionated radiotherapy (P = 0.003, 95% CI[14, 30]). DIBH also reduced the mean left lung dose by 10% in conventional radiotherapy (P = 0.010, 95% CI[2, 15]) and by 7% in hypofractionated radiotherapy (P = 0.008, 95% CI[0, 14]). Using the NTCP model to predict the risk of toxicities, DIBH reduced the predicted occurrence rates of pericarditis/pericardial effusion, RVD, CAD, and RTOG grade ≥ 2 RP by 35-39%, 46-51%, 4-7%, and 4-6%, respectively, across both treatment modalities. Contact_Heart was positively correlated with MHD but only weakly correlated with HI. Conclusion:DIBH significantly reduces radiation exposure to the heart and lungs in patients with left-sided breast cancer undergoing radiotherapy, potentially decreasing the risk of cardiopulmonary toxicity.

3640 Background: Neoadjuvant chemoradiotherapy (nCRT) combined with PD-1 blockade has shown great potential for improving the clinical complete response (cCR) rate in low rectal cancer patients in a previous exploratory clinical trial. This study aimed to further verify the efficacy and safety of nCRT with PD-1 blockade in patients with pMMR/MSS low rectal cancer. Methods: We conducted a prospective, multicenter, randomized, open-label trial (NCT05215379) with two parallel groups across 11 centers. Patients with confirmed cT 1-3a N 0-1 M 0 rectal adenocarcinoma of the pMMR/-MSS type, with an inferior margin of 5 cm from the anal verge, were randomly assigned to either the intervention group, receiving 50 Gy (2 Gy/d*25d) radiotherapy, 4 cycles of PD-1 blockade (sintilimab) and 2 cycles of CAPOX, or the control group, receiving 50 Gy (2 Gy/d*25d) radiotherapy and 2 cycles of CAPOX. After completion of neoadjuvant treatment, patients were reassessed to determine clinical efficacy. If patients achieved cCR, the Watch-and-Wait (W&W) approach was performed. If near-cCR (ncCR) was achieved, patients were re-evaluated by a multidisciplinary team to determine whether W&W, local excision, or total mesorectal excision (TME) should be performed. Patients assessed with an incomplete clinical response (iCR) received TME. The primary endpoint was the cCR rate, and the key secondary endpoints included the organ preservation (W&W and local excision) rate, disease free survival (DFS), and overall survival (OS). Results: From July 2022 to December 2024, a total of 201 patients with low rectal cancer were screened at 11 medical centers and 180 patients were finally randomly assigned. A total of 169 patients were included in the primary analysis (85 patients in the intervention group and 84 in control group). The cCR rates were 36.5% in the intervention group compared to 17.9% in the control group (p=0.007). There were 48 and 51 patients who underwent surgery in the intervention and control groups, respectively, and the pathological complete response (pCR) rates were 25.0% and 15.7% (p=0.249). The overall complete response (cCR+pCR) rates were 50.6% and 27.4% (p=0.002) in the intervention and control groups. The overall occurrence rates of adverse events (AEs) were 72.9% in the intervention group compared to 66.3% in the control group (P>0.05), and the occurrence of severe AEs (Grade III or IV) was comparable between the two groups (7.1% vs 3.8%, p=0.34). Due to the limited follow-up duration, the DFS and OS data are immature. Conclusion: In this trial, the results showed that nCRT combined with sintilimab significantly improved the cCR rate in patients with pMMR/MSS low rectal cancer with a tolerable safety profile. Clinical trial information: NCT05215379 .

To investigate MRI-based habitat analysis for its value in predicting pathologic response following neoadjuvant chemoradiotherapy (nCRT) in rectal cancer (RC) patients. 1021 RC patients in three hospitals were divided into the training and test sets (n = 319), the internal validation set (n = 317), and external validation sets 1 (n = 158) and 2 (n = 227). Deep learning was performed to automatically segment the entire lesion on high-resolution MRI. Simple linear iterative clustering was used to divide each tumor into subregions, from which radiomics features were extracted. The optimal number of clusters reflecting the diversity of the tumor ecosystem was determined. Finally, four models were developed: clinical, intratumoral heterogeneity (ITH)-based, radiomics, and fusion models. The performance of these models was evaluated. The impact of nCRT on disease-free survival (DFS) was further analyzed. The Delong test revealed the fusion model (AUCs of 0.867, 0.851, 0.852, and 0.818 in the four cohorts, respectively), the radiomics model (0.831, 0.694, 0.753, and 0.705, respectively), and the ITH model (0.790, 0.786, 0.759, and 0.722, respectively) were all superior to the clinical model (0.790, 0.605, 0.735, and 0.704, respectively). However, no significant differences were detected between the fusion and ITH models. Patients stratified using the fusion model showed significant differences in DFS between the good and poor response groups (all p < 0.05 in the four sets). The fusion model combining clinical factors, radiomics features, and ITH features may help predict pathologic response in RC cases receiving nCRT. Question Identifying rectal cancer (RC) patients likely to benefit from neoadjuvant chemoradiotherapy (nCRT) before treatment is crucial. Findings The fusion model shows the best performance in predicting response after neoadjuvant chemoradiotherapy. Clinical relevance The fusion model integrates clinical characteristics, radiomics features, and intratumoral heterogeneity (ITH)features, which can be applied for the prediction of response to nCRT in RC patients, offering potential benefits in terms of personalized treatment strategies.

To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimination of the TN stage of rectal cancer by different readers and deep learning classification models, compared with conventional MR images without DLR. Images of high-resolution T2-weighted, diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI) from patients with pathologically diagnosed rectal cancer were retrospectively processed with and without DLR and assessed by five readers. The first two readers measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the lesions. The overall image quality and lesion display performance for each sequence with and without DLR were independently scored using a five-point scale, and the TN stage of rectal cancer lesions was evaluated by the other three readers. Fifty of the patients were randomly selected to further make a comparison between DLR and traditional denoising filter. Deep learning classification models were developed and compared for the TN stage. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the proposed model. Overall, 178 patients were evaluated. The SNR and CNR of the lesion on images with DLR were significantly higher than those without DLR, for T2WI, DWI and CE-T1WI, respectively (p < 0.0001). A significant difference was observed in overall image quality and lesion display performance between images with and without DLR (p < 0.0001). The image quality scores, SNR, and CNR values of DLR image set were significantly larger than those of original and filter enhancement image sets (all p values < 0.05) for all the three sequences, respectively. The deep learning classification models with DLR achieved good discrimination of the TN stage, with area under the curve (AUC) values of 0.937 (95

To explore the lateral mesorectum structures and develop a nomogram model for predicting the prognosis of rectal cancer (RC) patients using preoperative high-resolution magnetic resonance imaging (MRI). Patients who underwent radical resection of RC in our hospital from January 2017 to December 2018 were retrospectively analyzed. Imaging data and postoperative 3-year prognosis data of patients were collected. The lateral mesorectum was observed, and related parameters were investigated: lateral interruption of the mesorectal fascia (LI-MRF), type of the middle rectal artery (MRA), and the maximum diameter of the MRA. The impact of lateral mesorectum parameters on prognosis was determined using Cox analysis and Kaplan–Meier (KM) survival curves. A nomogram combining lateral mesorectum parameters with clinical data was constructed and its predictive performance was validated. A total of 260 patients were included in this study. In preoperative high-resolution MRI, LI-MRF and MRA were observed bilaterally in all patients. Multivariate Cox regression analysis showed that the maximum diameter of the right MRA (P = 0.001) and right LI-MRF (P = 0.016) were predictive factors for postoperative 3-year overall survival (OS). Additionally, gender (P = 0.015), mrT stage (P = 0.025), and the maximum diameter of the right MRA (P = 0.002) were predictive factors for postoperative 3-year disease-free survival (DFS). The concordance indexes (C-index) of the predictive nomogram were 0.737 for OS and 0.685 for DFS. Preoperative high-resolution MRI revealed that the lateral mesorectum and MRA were inherent. The right LI-MRF and the maximum diameter of the right MRA were risk factors for poor postoperative survival in RC patients.

Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients. This retrospective study enrolled 321 rectal cancer patients (training set: 212; internal testing set: 53; external testing set: 56) who directly received total mesorectal excision from five hospitals between March 2014 to April 2021. A multitask deep learning model was developed to simultaneously predict recurrence/metastasis and disease-free survival (DFS). The model integrated clinicopathologic data and multiparametric magnetic resonance imaging (MRI) images including diffusion kurtosis imaging (DKI), without performing tumor segmentation. The receiver operating characteristic (ROC) curve and Harrell’s concordance index (C-index) were used to evaluate the predictive performance of the proposed model. The deep learning model achieved good discrimination capability of recurrence/metastasis, with area under the curve (AUC) values of 0.885, 0.846, and 0.797 in the training, internal testing and external testing sets, respectively. Furthermore, the model successfully predicted DFS in the training set (C-index: 0.812), internal testing set (C-index: 0.794), and external testing set (C-index: 0.733), and classified patients into significantly distinct high- and low-risk groups (p < 0.05). The multitask deep learning model, incorporating clinicopathologic data and multiparametric MRI, effectively predicted both recurrence/metastasis and survival for patients with rectal cancer. It has the potential to be an essential tool for risk stratification, and assist in making individualized treatment decisions. Not applicable.

PURPOSE:This paper describes the implementation of an instantaneous low-dose-rate total body irradiation (TBI) technique using block-filtered 6 MV X-rays with a linear accelerator (LINAC) to reduce pulmonary toxicity. METHODS:In the absence of dedicated TBI-specific meter-set dose rates in LINAC and sufficient treatment room size, a 2-cm-thick transmission block was used together with a 200-cm source-to-surface distance (SSD) to reduce the instantaneous dose rates of 6 MV x-rays down to 10 cGy/min, thus alteration to the beam properties. A TBI-specific dose calculation model was built with data acquired at the treatment planning system (TPS)-permitted maximum 140-cm SSD and was validated in phantoms at a 180-cm SSD. As for planning strategies, we adopted large anterior-to-posterior/posterior-to-anterior (AP/PA) open fields with multi-leaf collimator shielding for lungs to achieve target coverage, lung protection, and efficient dose delivery. A custom-designed sliding couch (Patent No. ZL202123085880.1) was manufactured to support patients during treatment. Measures to control the quality and safety of TBI treatment include machine interlocks, pretreatment checklists, and in-vivo dose monitoring. RESULTS:The instantaneous dose rate of block-filtered 6MV X-ray was reduced to approximately 7.0 cGy/min at 12.5-7.5 cm depth with a 185-200 cm SSD. The dose calculated by TPS differs from the measurements by 0.15%-1.55% in the homogeneous phantom and 1.2%-4.85% in the CIRS thorax phantom. The open-field TBI technique achieved V90% (PTV) ≈ 96.8% and MLD = 6.6 Gy with 1-h planning and 50-min beam delivery in a single fraction. From February 2021 to July 2023, 30 patients received TBI treatments in our center, and in-vivo monitoring results differed from TPS calculations by -1.49%-2.10%. After 6-12 months of follow-ups, all the patients treated in our center showed no pulmonary toxicities of grade 2 or higher. CONCLUSION:A low instantaneous dose rate TBI technique can be implemented in the clinic.

To build and validate a radiomics nomogram based on preoperative CT scans and clinical data for detecting synchronous ovarian metastasis (SOM) in female gastric cancer (GC) cases. Pathologically confirmed GC cases in 2 cohorts were retrospectively enrolled. All cases had presurgical abdominal contrast-enhanced CT and pelvis contrast-enhanced MRI and pathological examinations for any suspicious ovarian lesions detected by MRI. Cohort 1 cases (n = 101) were included as the training set. Radiomics features were obtained to develop a radscore. A nomogram combining the radscore and clinical factors was built to detect SOM. The bootstrap method was carried out in cohort 1 as internal validation. External validation was carried out in cohort 2 (n = 46). Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and the confusion matrix were utilized to assess the performances of the radscore, nomogram and subjective evaluation model. The nomogram, which combined age and the radscore, displayed a higher AUC than the radscore and subjective evaluation (0.910 vs 0.827 vs 0.773) in the training cohort. In the external validation cohort, the nomogram also had a higher AUC than the radscore and subjective evaluation (0.850 vs 0.790 vs 0.675). DCA and the confusion matrix confirmed the nomogram was superior to the radscore in both cohorts. This pilot study showed that a nomogram model combining the radscore and clinical characteristics is useful in detecting SOM in female GC cases. It may be applied to improve clinical treatment and is superior to subjective evaluation or the radscore alone.

Purpose This study aimed to develop a postoperative MRI-based fibrosis scoring system and to assess its correlation with anorectal function in locally advanced rectal cancer (LARC) cases administered neoadjuvant chemoradiotherapy (nCRT).Methods Pathologically confirmed LARC cases administered nCRT and radical resection were assessed retrospectively. Based on postoperative magnetic resonance imaging (MRI) findings, anastomotic fibrosis score (AFS) and perirectal fibrosis score (PFS) were determined to evaluate the extent of fibrosis. The Wexner continence score for anorectal function was obtained 2 years postoperatively and assessed for correlation with MRI fibrosis scores. The cases were divided into 2 groups by the median Wexner score. Univariable and multivariable analyses were adopted for building a nomogram model, whose diagnostic performance was estimated by receiver operating characteristic (ROC) and decision curve analyses (DCA).Results Finally, 144 patients with LARC were included in cohort 1 (training set). 52 patients were enrolled in cohort 2 (external validation set). Spearman correlation analysis indicated that AFS and PFS were positively correlated with the Wexner score. Univariable and multivariable analyses revealed age, tumor height, AFS, and PFS were independent predictors of anorectal function. The nomogram model achieved a good diagnostic performance, with AUCs of 0.800 and 0.827 in the training and validation sets, respectively; its predicting value was also confirmed by DCA.Conclusion The present study showed AFS and PFS derived from postoperative MRI are positively correlated with Wexner score. In addition, the new scoring system was effective in predicting anorectal function in LARC cases administered nCRT.

BackgroundThe objective of this study was twofold: firstly, to develop a convolutional neural network (CNN) for automatic segmentation of rectal cancer (RC) lesions, and secondly, to construct classification models to differentiate between different T-stages of RC. Additionally, it was attempted to investigate the potential benefits of rectal filling in improving the performance of deep learning (DL) models.MethodsA retrospective study was conducted, including 317 consecutive patients with RC who underwent MRI scans. The datasets were randomly divided into a training set (n = 265) and a test set (n = 52). Initially, an automatic segmentation model based on T2-weighted imaging (T2WI) was constructed using nn-UNet. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, three types of DL-models were constructed: Model 1 trained on the total training dataset, Model 2 trained on the rectal-filling dataset, and Model 3 trained on the non-filling dataset. The diagnostic values were evaluated and compared using receiver operating characteristic (ROC) curve analysis, confusion matrix, net reclassification index (NRI), and decision curve analysis (DCA).ResultsThe automatic segmentation showed excellent performance. The rectal-filling dataset exhibited superior results in terms of DSC and ASD (p = 0.006 and 0.017). The DL-models demonstrated significantly superior classification performance to the subjective evaluation in predicting T-stages for all test datasets (all p < 0.05). Among the models, Model 1 showcased the highest overall performance, with an area under the curve (AUC) of 0.958 and an accuracy of 0.962 in the filling test dataset.ConclusionThis study highlighted the utility of DL-based automatic segmentation and classification models for preoperative T-stage assessment of RC on T2WI, particularly in the rectal-filling dataset. Compared with subjective evaluation, the models exhibited superior performance, suggesting their noticeable potential for enhancing clinical diagnosis and treatment practices.

Background:Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC.Materials and methods:Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively.Results:The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734).Conclusion:The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care.Research registration unique identifying number UIN:Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.

目的:探讨基于M R T2 WI的影像组学方法对直肠癌接受新辅助治疗(nCRT)后病理完全反应(pC R)状态的评估价值.方法:回顾性分析2019年1月-2020年12月在我院接受新辅助放化疗(nCRT)后行手术切除的99例局部进展期直肠癌(locally-advanced rectal cancer,LARC)患者的病例资料.根据术后病理检查结果,分为pC R组(22例)及非pC R组(77例).在高分辨率T 2 W I上勾画病灶的容积感兴趣区(volume of interest,VOI)并提取其组学特征,采用最小绝对收缩和选择算子(LASSO)算法进行特征降维,筛选出与pC R相关的最佳组学特征.将所有病例按照7:3的比例随机分为两组:训练集(68例)和测试集(31例),建立支持向量机(support vector machine,SVM)机器学习模型,绘制其ROC曲线,并计算曲线下面积(AUC)、敏感度和特异度.结果:共提取1409个组学特征,经降维后得到11个最有价值的组学特征.建立的SVM机器学习模型在测试集中预测pCR的AUC为0.798(95％C I:0.615～0.920),符合率为83.87％,敏感度为85.71％,特异度为83.33％.结论:基于高分辨T2 WI的影像组学特征有助于预测nCRT后直肠癌pCR状态,可指导临床决策.

Background We aimed at determining the safety and feasibility of spot-scanning carbon ion radiotherapy (CIRT) for patients with localized prostate cancer. Methods We enrolled 118 patients with localized prostate cancer who underwent treatment with spot-scanning CIRT at the Shanghai Proton and Heavy Ion Center (SPHIC) from January 2016 to December 2020. The dose was gradually increased from relative biological effectiveness (RBE)-weighted dose (DRBE) = 59.2–65.6 Gy in 16 fractions. The primary endpoint was the occurrence of acute and late toxicities, while the secondary endpoints were biochemical relapse-free survival (bRFS), distant metastasis-free survival (DMFS), prostate cancer-specific survival (PCSS), and overall survival (OS). Results The median follow-up time was 30.2 months (4.8–62.7 months). Acute grade 1 and 2 genitourinary (GU) toxicities were 15.3% and 18.6%, while acute grade 1 and 2 gastrointestinal (GI) toxicities were 2.5% and 0%, respectively. Late grade 1 and 2 GU toxicities were 4.2% and 1.7%, respectively. No late GI toxicity was observed. Moreover, there were no cases of severe acute or late toxicity (≥ grade 3). No significant association were observed between the factors and the acute GU toxicities, except for clinical target volume (CTV) (p = 0.031) on multivariate analysis. The 2-year bRFS, DMFS, PCSS, and OS were 100%, 100%, 100%, and 98.8%, respectively. Conclusion The 2-year outcomes were encouraging, providing additional and useful information on the feasibility and safety of spot-scanning CIRT for treating prostate cancer. Thus, we recommend long-term follow-up and prospective multicentered studies to reinforce the role of CIRT in the management of localized prostate cancer.

目的:探讨基于高分辨T2WI的影像组学模型对评估直肠癌新辅助治疗疗效的价值.方法:回顾性分析2018年1月-2018年12月经手术病理证实且在接受新辅助治疗前、后均行MRI检查的80例直肠癌患者的病例资料.根据术后病理检查确定的肿瘤退缩分级(TRG),将TRG为0、1级者纳入疗效良好组,2、3级者纳入疗效不良组.在高分辨T2 WI上勾画病灶的三维容积兴趣区(VOI)并使用两种模型提取影像组学特征,模型1:仅提取治疗前基线影像组学特征;模型B2:提取基线和治疗后的影像组学特征.随机选取70％的病例作为训练集,30％的病例作为测试集进行验证.对两种模型分别利用LASSO(least absolute shrinkage and selection operator)算法进行特征降维后,与TRG标签建立随机森林(RF)分类器,并分别进行受试者操作特征(ROC)曲线分析,比较两种模型的曲线下面积(AUC)并分析其诊断效能(敏感度、特异度、准确度、阳性预测值、阴性预测值、阳性似然比、阴性似然比).采用决策曲线分析(DCA)评估临床获益.结果:模型1经降维后得到28个组学特征,模型2共获得3个组学特征,分别建立RF分类器模型,ROC曲线分析得到测试集模型1、2的AUC分别为0.943和0.950,两者间的差异无统计学意义(P>0.05).模型1的特异度及阳性似然比较高,模型B的敏感度及阴性似然比较高.DCA显示总体上两种方法均可以临床获益.结论:基于治疗前及综合治疗前、后MR T2 WI高分辨率图像的影像组学模型均可较准确地预测直肠癌新辅助治疗后的肿瘤退缩程度,可应用于临床上对直肠癌新辅助治疗疗效的评估.

For modeling prediction of the biological effectiveness of nanoparticle radiosensitization, an extended local effects model is first proposed in the start of this work, to give a more comprehensive description of nanoparticle mediated dose survival modification. With the local dose enhancement effects in both nucleus and cytoplasm, and the local-to-global biochemical effects taken into account, the extended local effects model presents more complete mechanisms of nanoparticle mediated killing enhancements with strong subcellular inhomogeneities, but meanwhile puts forward some fundamental issues that need further studies, including the extranuclear local dose effects, and the modeling methodologies for the local-to-global biochemical effects. To avoid the modeling difficulty, in the second part, a survival modification framework of compound Poisson additive killing is proposed for nanoparticle radiosensitization. The framework can be summarized as independent additive killing by the equivalent uniform doses of each individual particles per cell following the LQ model, thus leading to a LQ parameter modification of the dose-survival relationship. Based on the framework, a compound Poisson killing model is constructed for an analytical prediction of LQ parameter modification. Simplified form of the model is derived as concentration dependent correction only to α parameter, with (α”/α) value dominated by the mean number and modified by the agglomeration of the particles per cell. For practical use, two operational forms of the model are recommended for interpolation prediction of the correction (α”/α) using different η. In the last part, initial validation are presented by a carbon dots radiosensitization of HepG2 cells. For the (α”/α) values at the intermediate concentrations, the dose survival demonstrated a good accuracy of the monodispersion form with η=1. The prediction errors are presented in two different forms, both within 5% on average, and not exceeding 10% at the maximum.

Abstract Background To explore the diagnostic value of three different measurement approaches in differentiating T1a–T1b from T2 gastric cancer (GC) lesions. Methods A total of 95 consecutive patients with T1a–T2 stage of GC who performed preoperative MRI were retrospectively enrolled between January 2017 and November 2020. The parameters MRI T stage (subjective evaluation), thickness, maximum area and volume of the lesions were evaluated by two radiologists. Specific indicators including AUC, optimal cutoff, sensitivity, specificity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), positive predictive value (PPV) and negative predictive value (NPV) of MRI T stage, thickness, maximum area and volume for differentiating T1a–T1b from T2 stage lesions were calculated. The ROC curves were compared by the Delong test. Decision curve analysis (DCA) was used to evaluate the clinical benefit. Results The ROC curves for thickness (AUC = 0.926), maximum area (AUC = 0.902) and volume (AUC = 0.897) were all significantly better than those of the MRI T stage (AUC = 0.807) in differentiating T1a–T1b from T2 lesions, with p values of 0.004, 0.034 and 0.041, respectively. The values corresponding to the thickness (including AUC, sensitivity, specificity, accuracy, PPV, NPV, PLR and NLR) were all higher than those corresponding to the MRI T stage, maximum area and volume. The DCA curves indicated that the parameter thickness could provide the highest clinical benefit if the threshold probability was above 35%. Conclusions Thickness may provide an efficient approach to rapidly distinguish T1a–T1b from T2 stage GC lesions.

PURPOSE:The purpose of this study was to assess the potential of 99mTc-labeled PSMA-SPECT/CT and diffusion-weighted image (DWI) for predicting treatment response after carbon ion radiotherapy (CIRT) in prostate cancer.PATIENTS AND METHODS:We prospectively registered 26 patients with localized prostate cancer treated with CIRT. All patients underwent 99mTc-labeled PSMA-SPECT/CT and multiparametric magnetic resonance imaging (MRI) before and after CIRT. The tumor/background ratio (TBR) and mean apparent diffusion coefficient (ADCmean) were measured on the tumor and the percentage changes before and after therapy (ΔTBR and ΔADCmean) were calculated. Patients were divided into two groups: good response and poor response according to clinical follow-up.RESULTS:The median follow up time was 38.3months. The TBR was significantly decreased (p=0.001), while the ADCmean was significantly increased compared with the pretreatment value (p<0.001). The ΔTBR and ΔADCmean were negatively correlated with each other (p = 0.002). On ROC curve analysis for predicting treatment response, the area under the ROC curve (AUC) of ΔTBR (0.867) for predicting good response was higher than that of ΔADCmean (0.819). The AUC of combined with ΔTBR and ΔADCmean (0.895) was higher than that of either ΔADCmean or ΔTBR alone. The combined use of ΔTBR and ΔADCmean showed 91.4% sensitivity and 95.2% specificity.CONCLUSION:Our preliminary data indicate that the changes of TBR and ADCmean maybe an early bio-marker for predicting prognosis after CIRT in localized prostate cancer patients. In addition, the ΔTBR seems to be a more powerful prognostic factor than ΔADCmean in prostate cancer treated with CIRT.

Purpose: The purpose of this study was to prospectively analyze the safety and feasibility of spot scanning carbon ion radiotherapy (CIRT) for patients with localized prostate cancer. Methods: 118 localized prostate cancer patients treated with spot scanning CIRT at Shanghai Proton and Heavy Ion Center (SPHIC) were enrolled in this dose escalated study. The dose was gradually increased from 59.2GyE to 65.6GyE in 16 fractions. The primary endpoint was the acute and late toxicities. Secondary endpoints were biochemical relapse free survival (bRFS), distant metastasis free survival (DMFS), prostate cancer-specific survival (PCSS), and overall survival (OS). Results: The median follow-up time was 30.2 months (4.8-62.7 months). Acute grade 1 and 2 genitourinary (GU) toxicities were 15.3% and 18.6%, while acute grade 1 and 2 gastrointestinal (GI) toxicities were 2.5% and 0%, respectively. Late grade 1 and 2 GU toxicities were 4.2% and 1.7%, respectively. No late GI toxicity were observed. There were no cases of severe acute or late toxicity (≥grade 3). The significant association was not found between the factors and the acute GU toxicities except for CTV volume (p=0.031) on multivariate analysis. The 2-year bRFS, DMFS, PCSS, OS were 100%, 100%, 100% and 98.8%, respectively. Conclusion: The 2 years’ outcomes are encouraging, providing additional and useful information on the feasibility and safety of spot scanning CIRT for prostate cancer. Long term follow-up and prospective multi-institutional data are warranted to reinforce the role of CIRT in the management of localized prostate cancer. Trial registration: Clinicaltrial, NCT02739659. Registered 15 April 2016

1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China; 3Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, People’s Republic of China; 4Institute of Modern Physics, Fudan University, Shanghai, People's Republic of China; 5Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, People’s Republic of China; 6Department of Radiation Oncology, Shanghai Concord Cancer Hospital, Shanghai, People’s Republic of China Background: Radiotherapy is the main treatment for localized prostate cancer. The ther-

Background In cancer radiotherapy, microbeam is an advanced and effective tool in investigating radiobiology. Currently, evidence to support the radiobiology of proton beam radiotherapy for prostate cancer is limited. This study aimed to investigate the DNA damage response of proton microbeam irradiation in prostate cancer. Methods Single-particle irradiation system to cells (SPICE) was used to perform the proton microbeam radiation-induced DNA damage response. The SPICE can deliver defined number of protons (3.4 MeV) to the cell nucleus. Different quantities of protons were irradiated to observe differential dose responses in prostate cancer cells. A total of 500 protons or defined proton doses were applied to PC-3 cell nucleus to investigate the kinetics of DNA double-strand breaks (DSB) repair after different time intervals; between 1 and 24 h post-irradiation. Subsequently, immunofluorescent staining of γ-H2AX was performed to detect DSB, and images were captured by immunofluorescence microscopy. Finally, γ-H2AX fluorescence intensity in each nucleus was quantified with Image J software. Results Proton microbeam radiation-induced DSB were dependent on proton dose applied. After irradiated with 500 protons, relative expression levels of γ-H2AX were time dependent during DSB repair process. The γ-H2AX fluorescence intensity was maximum at 1 h post-irradiation. However, a gradual decrease was observed from 4 to 24 h. Conclusions Microbeam is a valuable tool for the exploration of DSB response. The findings of the present study show that microbeam irradiation targeted the nucleus with precision. This study is the first to reveal that immune-stained γ-H2AX assay with proton microbeam irradiation could predict DSB repair kinetics in PC-3 cells.