Purpose To investigate the diagnostic performance of virtual calcium imaging (VCI) parameters from photon-counting detector (PCD) CT for assessing lumbar vertebral osteoporosis. Materials and Methods A phantom study was first conducted using standard- and low-dose PCD CT to assess the stability and reproducibility of VCI parameters. Participants who underwent dual-energy x-ray absorptiometry (DXA), the reference standard for osteoporosis assessment, and standard- or low-dose PCD CT were prospectively enrolled. Partial correlation coefficients were used to assess associations between VCI parameters and DXA-derived T-scores. Measurements were obtained from anterior, middle, and posterior vertebral subregions. Diagnostic performance for osteoporosis was evaluated using receiver operating characteristic analysis. Results In the phantom study, VCI parameters from standard- and low-dose PCD CT showed strong positive correlations with known bone density (r = 0.94-0.95; P < .001). A total of 94 participants (mean age, 69.82 years ± 9.39 [SD]; 55 female) in the low-dose group and 91 participants (mean age, 69.52 years ± 9.32; 53 female) in the standard-dose group were included in the analysis. In the low-dose group, CT attenuation for calcium (Cal), calcium density, and virtual monoenergetic 70 keV (ME70) attenuation values were significantly correlated with T-scores after adjustment for sex and body mass index (r = 0.64, 0.67, and 0.62, respectively; all P < .001). These parameters differed significantly across vertebral subregions and bone mass categories (normal, reduced, and osteoporosis; P < .001). Using thresholds of Cal of 133.70 HU or less, calcium density of 4.93 mg/cm3 or less, and ME70 of 112.43 HU or less, receiver operating characteristic analysis demonstrated good diagnostic performance for osteoporosis (areas under the receiver operating characteristic curve, 0.90, 0.91, and 0.89, respectively), with sensitivities of 85%, 87%, and 82% and specificities of 83%, 79%, and 84%, respectively. Similar results were observed in the standard-dose group. Conclusion VCI parameters from PCD CT showed strong correlations with DXA T-scores and demonstrated high diagnostic performance for osteoporosis, suggesting a promising opportunistic screening tool for lumbar vertebral bone density assessment. Keywords: CT Photon Counting, CT Dual Energy, Thorax, Spine Supplemental material is available for this article. © RSNA, 2026.

BACKGROUND AND PURPOSE:Lenticulostriate arteries (LSAs) are micro but clinically critical perforating vessels that remain difficult to visualize using conventional techniques. This study aimed to conduct a preliminary comparison of the visualization of LSAs in head and neck CTA with photon-counting detector CT (PCD-CT) with 4 reconstruction modes. MATERIALS AND METHODS:This retrospective study included patients who underwent head and neck CTA with PCD-CT between January 2024 and May 2024 and those who underwent CTA with energy-integrating detector CT (EID-CT) between September 2023 and May 2024. The images were reconstructed in 4 modes for the PCD-CT group and 1 mode for the EID-CT group. The image quality was evaluated, and the LSAs were visualized by using subjective evaluations and quantitative assessments. The Friedman test was used to compare the characteristics of the 4 reconstruction modes. RESULTS:The PCD-CT group comprised 56 patients (average age 65 ± 14 years; 28 men), and the EID-CT group comprised 28 patients (average age, 64 ± 12 years; 14 men). In the PCD-CT group, the 0.2-mm slice-thickness threshold 3D mode displayed the highest counts of LSAs (right side: 4.00 [range, 3.00-4.00]; left side: 4.00 [range, 3.00-4.00]) and the longest LSAs (right side: 23.33 ± 4.86 mm; left side: 24.18 ± 4.51 mm). The 0.4-mm slice-thickness vascular spectral postprocessing mode obtained higher subjective evaluation scores. Furthermore, the PCD-CT group exhibited a higher count of LSAs, longer LSA lengths, and superior image quality than the EID-CT group. CONCLUSIONS:PCD-CT achieves better visualization of LSAs and image quality than EID-CT and therefore constitutes an important method for observing LSAs in the future.

Current coronary CT angiography (CCTA)-based methods have limited accuracy in identifying myocardial ischemia. Radiomics may help to provide a more accurate assessment. To investigate the diagnostic and reclassification value of myocardial radiomic signatures combined with CCTA-derived fractional flow reserve (CT-FFR) and stenosis quantification in detecting hemodynamically significant coronary artery disease (CAD). Consecutive symptomatic patients clinically referred for CCTA and invasive coronary angiography (ICA) from five medical centers were prospectively recruited. The datasets were randomly divided into a training and internal validation cohort in a 7:3 ratio from three centers, the remaining patients were enrolled in the external testing cohort. The radiomics features were extracted from the vessel-related myocardium, Boruta and Random-forest was applied for further feature selection and model construction. A traditional model was established combining CT-FFR with CCTA quantification and a hybrid model was further developed by adding radiomics signatures. A total of 504 vessel-based myocardial territories from 226 patients underwent analysis. Compared with traditional model, the hybrid model showed superior discrimination of flow-limiting CAD in the training or validation cohort (AUC: 0.81 vs. 0.73 and 0.79 vs. 0.74; all P<.05). In the testing cohort, with radiomics added, it exhibited significant reclassification performance (NRI=0.46, P<.001), especially in non-flow-limiting CAD discrimination within the CCTA positive group. (NRI=0.21, P<.001). Combined with radiomics, the hybrid model outperformed the traditional approach for detecting myocardial ischemia. The radiomics further improved reclassification performance in patients with obstructive stenosis in CCTA.

Objective To develop a diagnostic model combining the CT angiography(CCTA)-derived myocardial radiomics signatures with the CT-derived fractional flow reserve(CT-FFR)based on coronary CCTA and investigate the diagnostic accuracy of the hybrid model for hemodynamically significant coronary artery disease(CAD).Methods The patients presenting stable angina pectoris,diagnosed with CAD,and clinically referred for CCTA examination and invasive coronary angiography were prospectively recruited.Radiomics features of the left ventricular myocardium were extracted from the three main perfusion territories demarcated according to the coronary blood supply.The extracted features were first selected by the minimum redundancy maximum relevance feature ranking method.A least absolute shrinkage and selection operator Logistic regression algorithm with leave-one-out cross-validation was then employed to construct a radiomics model.The CT-FFR value was generated for each blood vessel.The area under the receiver operating characteristics curve(AUC_ROC),sensitivity,and specificity were adopted to evaluate the performance of each model against the reference standard invasive coronary angiography/FFR.Results A total of 70 patients[42 men and 28 women;(61±10) years old] were included in this study and complemented CCTA examination,with 175 vessels and the corresponding myocardial territories undergoing invasive coronary angiography/FFR.A total of 1 656 specific radiomics parameters were extracted,from which 14 features were selected to establish the radiomics model.The AUC_ROC,sensitivity,and specificity were 0.797(95%CI=0.732-0.861),77.1%,and 73.7%for the radiomics model,0.892(95%CI=0.841-0.943),81.4%,and 88.8%for the CT-FFR model,and 0.928(95%CI=0.890-0.965),83.3%,and 88.4%for the hybrid model,respectively.The hybrid model outperformed the radiomics model and CT-FFR alone(P=0.040).Conclusions The radiomics signatures of the vessel-related myocardium from CCTA could provide incremental value to the diagnostic performance of CT-FFR and improve vessel-specific ischemia detection.The hybrid model combining CT-FFR with radiomics signatures is potentially feasible for improving the diagnostic accuracy for hemodynamically significant CAD.

Background A non-invasive tool for tumor regression grade (TRG) evaluation is urgently needed for gastric cancer (GC) treated with neoadjuvant chemotherapy (NAC). Purpose To develop and validate a radiomics signature (RS) to evaluate TRG for locally advanced GC after NAC and assess its prognostic value. Material and Methods A total of 103 patients with GC treated with NAC were retrospectively recruited from April 2018 to December 2019 and were randomly allocated into a training cohort (n = 69) and a validation cohort (n = 34). Delineation was performed on both mixed and iodine-uptake images based on dual-energy computed tomography (DECT). A total of 4094 radiomics features were extracted from the pre-NAC, post-NAC, and delta feature sets. Spearman correlation and the least absolute shrinkage and selection operator were used for dimensionality reduction. Multivariable logistic regression was used for TRG evaluation and generated the optimal RS. Kaplan–Meier survival analysis with the log-rank test was implemented in an independent cohort of 40 patients to validate the prognostic value of the optimal RS. Results Three, five, and six radiomics features were finally selected for the pre-NAC, post-NAC, and delta feature sets. The delta model demonstrated the best performance in assessing TRG in both the training and the validation cohorts (AUCs=0.91 and 0.76, respectively; P>0.1). The optimal RS from the delta model showed a significant capability to predict survival in the independent cohort ( P<0.05). Conclusion Delta radiomics based on DECT images serves as a potential biomarker for TRG evaluation and shows prognostic value for patients with GC treated with NAC.

OBJECTIVES To evaluate the feasibility of using coronary computed tomography angiography (CCTA)-derived strain to detect regional myocardial dysfunction in coronary artery disease (CAD) patients with normal left ventricular ejection fraction (LVEF). METHODS A total of 1,580 segments from 101 patients who underwent stressed CT myocardial perfusion imaging (CT-MPI) and CCTA were retrospectively enrolled in this study. The CT-derived global and segmental strain values were evaluated using the feature tracking technique. Segments with myocardial blood flow (MBF) <  125 ml/min/100 ml and 95 ml/min/100 ml were categorized as ischemic and infarcted, respectively. RESULTS Segmental radial strain (SRS) and segmental circumferential strain (SCS) in the abnormal segments (including all segments with MBF <  125 ml/min/100 ml) were significantly lower than those in the normal segments (14.81±8.65% vs 17.17±9.13%, p <  0.001; -10.21±5.79% vs -11.86±4.52%, p <  0.001, respectively). SRS and SCS values in infarcted segments were significantly impaired compared with the ischemic segments (12.43±8.03% vs. 15.32±8.71%, p = 0.038; -7.72±5.91% vs. -10.67±5.66%, p = 0.010, respectively). The AUCs for SRS and SCS in detecting infarcted segments were 0.622 and 0.698, respectively (p <  0.05). CONCLUSIONS It is feasible for using CCTA-derived strain parameters to detect regional myocardial dysfunction in CAD patients with preserved LVEF. Segmental radial and circumferential strain have the potential ability to distinguish myocardial ischemia from infarction, and normal from ischemic myocardium.

Rationale and objectives: To develop and validate 2 iodine maps based radiomics nomograms for preoperatively predicting cervical lymph node metastasis (LNM) and central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC). Materials and methods: A total of 346 patients with PTC were enrolled and allocated to training (242) and validation (104) sets. Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Aggregated machine-learning strategy was applied for features selection and construction of 2 radiomics scores (LN rad-score; CLN rad-score). Logistic regression model was employed to establish two radiomics nomograms (nomogram 1: predicting LNM; nomogram 2: predicting CLNM) after incorporating LN or CLN rad-score with clinical predictors. Nomograms performance was determined by discrimination, calibration and clinical usefulness. Results: Nomogram 1 incorporated LN rad-score, age (categorized by 55) and CT reported LN status; Nomogram 2 incorporated CLN rad-score, capsule contact >25% and CT reported CLN status. 2 nomograms both showed good discrimination and calibration in the training (AUC = 0.847; AUC = 0.837) and validation cohorts (AUC = 0.807; AUC = 0.795). Significant improved AUC, net reclassification index (NRI) and integrated discriminatory improvement (IDI) confirmed additional great predictive value of 2 rad-scores, compared with clinical models without radiomics. Decision curve analysis indicated clinical utility of nomograms. 2 nomograms both demonstrated favorable predictive efficacy in CT reported LN or CLN negative subgroup (AUC = 0.766; AUC = 0.744). Conclusion: The presented 2 radiomics nomograms are useful tools for preoperative prediction of LNM and CLNM in PTC.

Objective To develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC). Patients and Methods This retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D’Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan–Meier curve and log-rank test for the high- and low-risk groups. Results The DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit ( P <0.05). The risk score, age, thyroid transcription factor-1, Ki-67, and disease stage were the independent risk factors for NSCLC.The Greenwood-Nam-D’Agostino test showed good calibration performance ( P =0.39). Both high- and low-risk patients did not benefit from adjuvant chemotherapy, and chemotherapy in low-risk groups may lead to a poorer prognosis. Conclusions The DeepSurv nomogram, which is based on the risk score and independent risk factors, had good predictive performance for survival outcome. Further, it could be used to guide personalized adjuvant chemotherapy in patients with NSCLC.

PurposeThis study aimed to evaluate the feasibility of differentiating the atrial fibrillation (AF) subtype and preliminary explore the prognostic value of AF recurrence after ablation using radiomics models based on epicardial adipose tissue around the left atrium (LA-EAT) of cardiac CT images. MethodThe cardiac CT images of 314 patients were collected wherein 251 and 63 cases were randomly enrolled in the training and validation cohorts, respectively. Mutual information and the random forest algorithm were used to screen for the radiomic features and construct the radiomics signature. Radiomics models reflecting the features of LA-EAT were built to differentiate the AF subtype, and the multivariable logistic regression model was adopted to integrate the radiomics signature and volume information. The same methodology and algorithm were applied to the radiomic features to explore the ability for predicting AF recurrence. ResultsThe predictive model constructed by integrating the radiomic features and volume information using a radiomics nomogram showed the best ability in differentiating AF subtype in the training [AUC, 0.915; 95% confidence interval (CI), 0.880-0.951] and validation (AUC, 0.853; 95% CI, 0.755-0.951) cohorts. The radiomic features have shown convincible predictive ability of AF recurrence in both training (AUC, 0.808; 95% CI, 0.750-0.866) and validation (AUC, 0.793; 95% CI, 0.654-0.931) cohorts. ConclusionsThe LA-EAT radiomic signatures are a promising tool in the differentiation of AF subtype and prediction of AF recurrence, which may have clinical implications in the early diagnosis of AF subtype and disease management.

Background Pancreatic fibrosis and fatty infiltration are associated with postoperative pancreatic fistula (POPF), but accurate preoperative assessment remains a challenge. Iodine concentration (IC) and fat fraction derived from dual-energy CT (DECT) may reflect the amount of fibrosis and steatosis, potentially enabling the preoperative prediction of POPF. Purpose To identify multiphasic DECT-derived IC and fat fraction that improve the prediction of POPF risks compared with contrast-enhanced CT attenuation values and to evaluate the underlying histopathologic changes. Materials and Methods This retrospective study included patients who underwent pancreatoduodenectomy and DECT (including pancreatic parenchymal, portal venous, and delayed phase scanning) between January 2020 and December 2020. The relationships of the quantitative DECT-derived IC and fat fraction, along with CT attenuation values from enhanced images with POPF risk, were analyzed with logistic regression analysis. The predictive performance of the IC was compared with that of the CT values. The histopathologic underpinnings of IC were evaluated with multivariable linear regression analysis. Results A total of 107 patients (median age, 65 years; interquartile range, 57-70 years; 56 men) were included. Of these, 23 (21%) had POPF. The pancreatic parenchymal-to-portal venous phase IC ratio (adjusted odds ratio [OR], 13; 95% CI: 2, 162; P < .001) was an independent predictor of POPF occurrence. The accuracy of the pancreatic parenchymal-to-portal venous phase IC ratio in predicting POPF was higher than that of the CT value ratio in the same phases (78% vs 65%, P < .001). The pancreatic parenchymal-to-portal venous phase IC ratio was independently associated with pancreatic fibrosis (β = -1.04; 95% CI: -0.44, -1.64; P = .001). Conclusion A higher pancreatic parenchymal-to-portal venous phase IC ratio was associated with less histologic fibrosis and greater risk of POPF. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Yoon in this issue.

Objective:To explore the diagnostic value of radiomics based on arterial-venous mixed images derived from dual-energy CT (DECT) data in diagnosis of cervical lymph nodes (LNs) metastasis of papillary thyroid cancer (PTC).Methods:From June 2017 to December 2018, eighty-four patients with preoperatively DECT scanning and pathologically confirmed PTC (129 non-metastatic LNs and 97 metastatic LNs) in the First Affiliated Hospital of Nanjing Medical University were included in this study. The clinical and imaging data of all patients were retrospectively analyzed. The training cohort consisted of 62 PTC cases with 156 LNs (91 non-metastatic LNs and 65 metastatic LNs). An independent validation cohort consisted of 22 PTC patients with 70 LNs (38 non-metastatic LNs and 32 metastatic LNs). Semi-automatic LNs segmentation was conducted on arterial-venous mixed images derived from DECT using Syngo.via Frontier Radiomics software. Totally 1 226 radiomics features were extracted from arterial-venous mixed images for each LN. The least absolute shrinkage and selection operator (LASSO) regression was applied for radiomics features selection and signature building. The logistic regression modeling was used to construct diagnostic models based on the CT image features of LNs (model 1), the radiomics signature (model 2) and the combination of the CT image features and radiomics signature (model 3). An intuitive nomogram was plotted for model 3. The ROC curve analyses and area under the curve (AUC) were performed to evaluate the diagnostic efficiency of the three models, with the performances compared using the Delong test.Results:Model 1 was developed with LNs shape, degree of enhancement, pattern of enhancement, calcification and extra nodal extension. Three arterial phase radiomics features were selected and used to establish radiomics signature using LASSO regression (model 2). Model 3 was developed with LNs size, shape, degree of enhancement and radiomics signature. In both the training and validation cohort, model 3 showed the best diagnostic performance (AUC=0.965, 0.933), followed by model 2 (AUC=0.947, 0.910), and both these two models significantly outperformed model 1 (AUC=0.850, 0.846) (training cohort, Z=4.066 and 3.758, P both<0.001; validation cohort, Z=2.871 and 1.998, P=0.017 and 0.042) respectively. Conclusion:The radiomics model based on arterial-venous mixed images derived from DECT data can realize effective diagnosis of LNs metastasis in patients with PTC; and the combination model of radiomics signature with CT image features can further improve the diagnostic accuracy.

Objective:To evaluate the diagnostic value of the combination of CT image features and quantitative dual-energy CT (DECT) parameters in diagnosing cervical lymph nodes (LNs) metastasis from papillary thyroid carcinoma (PTC).Methods:Preoperative DECT imaging data of 103 patients with pathologically proven PTC in the First Affiliated Hospital of Nanjing Medical University from June 2017 to June 2019 were retrospectively analyzed. Taking 2002 American Association of Head and Neck Surgery criteria for LNs division as a reference, cervical LNs were divided into 7 levels. A total of 245 LNs were enrolled using radiological-pathological subzone comparison method. According to pathological results, 107 LNs were classified as metastatic LNs group and 138 LNs were classified as non-metastatic LNs group. CT image features including size, shape, margin, degree of enhancement, pattern of enhancement, calcification, cystic change and extra nodal extension were assessed. Quantitative DECT parameters including standardized iodine concentration (NIC), standardized effective atomic number (Z eff-c) and slope of energy spectrum curve (λ HU) were calculated. The χ 2 test or Mann-Whitney U rank sum test were used to compare the difference of CT image features and quantitative parameters between the two groups. The multivariate logistic regression analysis was used to build models based on CT image features, quantitative DECT parameters and their combination. The ROC curve analyses were performed to evaluate the diagnostic efficiency. Results:Significant differences were found in all CT image features between metastatic and non-metastatic LNs groups ( P<0.05). All the arterial and venous phase DECT parameters in metastatic LNs group were higher than those in non-metastatic LNs group ( P<0.001). The area under curve (AUC), sensitivity, specificity of combination of logistic model based on both CT image features and quantitative DECT parameters were 0.922, 86.0% and 92.8% for diagnosing cervical LNs metastasis from PTC. The AUC value, sensitivity, specificity of logistic model based on quantitative DECT parameters were 0.912, 84.1% and 93.5%. Both of them outperformed logistic model based on CT image features with an AUC of 0.783, a sensitivity of 71.0% and a specificity of 73.9% ( Z=5.212, 4.554, P<0.001). Conclusions:Compared with CT image features, quantitative DECT parameters has better diagnostic performance in differentiating metastatic from non-metastatic LNs in patients with PTC. Integrating CT image features with quantitative dual-energy CT parameters together can furthermore improve the differentiating performance.

OBJECTIVE: To assess the feasibility of using virtual non-contrast (VNC) images derived from dual-energy computed tomography (DECT) to replace true non-contrast (TNC) images of papillary thyroid carcinoma (PTC) patients. METHODS: Images of 96 PTC patients were retrospectively analyzed. TNC images were acquired under the single-energy mode of DECT after the plain scanning. The arterial and venous phase VNC (VNC-a and VNC-v) images were generated by the post-processing algorithm from the arterial phase and venous phase of contrast-enhanced CT images, respectively. Mean attenuation values, image noise, number and length of calcification were measured. Radiation dose was also calculated. Last, subjective score of image quality was evaluated by a 5-point scale. RESULTS: Signal-to-noise ratio (SNR) of each tissue in TNC images is significantly higher than that of VNC images ( p<0.050). Contrast-to-noise ratio (CNR) of fat, muscle, thyroid nodules and internal carotid artery in TNC images is significantly higher than that of VNC images, while CNR in TNC images is lower for cervical vertebra ( p<0.001). Calcification is detected on TNC images of 44 patients, while it is omitted on VNC images of 14 patients (31.8%). The subjective score of TNC images is higher than VNC images ( p<0.001). The effective dose reduction is 47.6% by avoiding plain scanning. CONCLUSIONS: Considering the different attenuation value, SNR, CNR and especially reduced detection rate of calcification, we deem that VNC images cannot be directly used to replace TNC images in PTC patients, despite the reduced radiation dose.

Objective: We intend to evaluate the diagnostic efficacy of dual-energy CT radio-mics model based on Iodine Map (IM) in the application of preoperative re-staging of serosal invasion in locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC) treatment. Methods: A retrospective study was conducted on 155 patients with LAGC who were treated with standard NAC before operation (including 110 cases in training group and 45 cases in testing group). Two radiologists analyzed all the CT images and carried out the classification. After the semi-automatic drawing of region of interest volume (VOI), we extracted 1226 imaging features from each lesion based respectively on IM and 120kVp images. We adopted Spearman related analysis, Least Absolute Shrinkage and Selection Operator (LASSO) to punish Logistic regression in order to acquire important feature by getting rid of unstable and redundant features. Through multi-factor Logistic regression analysis, we established two prediction models (120kVp and IM-120kVp) based on the features selected respectively by 120kVp and 120kVp combined with IM. Results: Two radio-mics models both showed great prediction accuracy and efficiency in training and testing groups (IM-120kVp: AUC: training group, 0.953, testing group, 0.879; 120kVp: AUC: training group, 0.940, testing group, 0.831). The diagnostic accuracy of both models in the testing group (IM-120kVp: 84.4%, 120kVp: 80.0%) were higher than manual classification (68.9%). The diagnostic efficacy of IM-120kVp model was better than manual classification both in training (P<0.001) and testing groups (P=0.034). Conclusion: The radio-mics model based on dual-energy CT shows convincing diagnostic efficacy in differentiating serosal invasion in preoperative re-staging for LAGC patients after NAC treatment.

BACKGROUND:In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs).METHODS:The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS).RESULTS:The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell's concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2.CONCLUSIONS:CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

目的:分析基于冠状动脉(冠脉)CT血管成像(CTA)的血流储备分数(FFRCT)与斑块特征对冠心病(CAD)患者主要不良心脏事件(MACE)发生的预测价值.方法:回顾性搜集88例CAD患者冠脉CTA资料,测量其FFRCT和斑块特征,并随访CAD患者36个月内MACE发生情况.比较MACE阳性组和阴性组间冠脉解剖狭窄程度、FRCT和斑块特征的差异,采用多因素逻辑回归方法确定MACE相关的独立危险因素,并采用ROC曲线评价这些参数对MACE的预测价值.结果:MACE阳性组(17例共计17处责任病变)与阴性组(71例共计80支非责任血管)相比,FFRCT、管腔直径狭窄率、斑块最小管腔面积、斑块总体积、血管总体积、非钙化斑块体积、斑块长度、斑块负荷及低密度斑块出现率的差异均有统计学意义(P＜0.05).其中FFRCT≤0.8、管腔直径狭窄率＞60.5％、斑块负荷＞0.67、斑块长度＞19.4 mm及低密度斑块出现是MACE发生的独立预测因子.受试者操作特征(ROC)曲线分析中,上述独立预测因子联合后对MACE的预测价值最高[ROC曲线下面积(AUC)为0.821],冠脉解剖狭窄程度(管腔直径狭窄率＞60.5％)单一指标的预测价值最低(AUC为0.664).结论:基于冠脉CTA的解剖狭窄程度与FFRC.T斑块特征(斑块长度、斑块负荷和低密度斑块)联合后,组合指标对CAD患者发生MACE的预测价值优于冠脉解剖狭窄程度单一指标.

Background Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be time-consuming and unstable. Purpose To assess the value of a semi-automatic segmentation tool in improving the reproducibility of the radiomic features of GCLM. Material and Methods Patients who underwent dual-source computed tomography were retrospectively reviewed. As an intra-observer analysis, one radiologist segmented metastatic liver lesions manually and semi-automatically twice. Another radiologist re-segmented the lesions once as an inter-observer analysis. A total of 1691 features were extracted. Spearman rank correlation was used for feature reproducibility analysis. The times for manual and semi-automatic segmentation were recorded and analyzed. Results Seventy-two patients with 168 lesions were included. Most of the GCLM radiomic features became more reliable with the tool than the manual method. For the intra-observer feature reproducibility analysis of manual and semi-automatic segmentation, the rates of features with good reliability were 45.5% and 62.3% (P < 0.02), respectively; for the inter-observer analysis, the rates were 29.3% and 46.0% (P < 0.05), respectively. For feature types, the semi-automatic method increased reliability in 6/7 types in the intra-observer analysis and 5/7 types in the inter-observer analysis. For image types, the reliability of the square and exponential types was significantly increased. The mean time of semi-automatic segmentation was significantly shorter than that of the manual method (P < 0.05). Conclusion The application of semi-automated software increased feature reliability in the intra- and inter-observer analyses. The semi-automatic process took less time than the manual process.

Objectives The aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC). Methods A total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman’s correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared. Result Three radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p<0.001) and testing cohorts (p<0.001 and p=0.034, respectively). Conclusions Dual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.

Abstract Background Multiple guidelines for pancreatic ductal adenocarcinoma (PDAC) suggest that all stages of patients need to receive postoperative adjuvant chemotherapy. S-1 is a recently emerged oral antitumour agent recommended by the guidelines. However, which population would benefit from S-1 needs to be determined, and predictors of chemotherapy response are needed for personalized precision medicine. This pilot study aimed to initially identify whether whole-tumour evaluation with MRI and radiomics features could be used for predicting the efficacy of S-1 and to find potential predictors of the efficacy of S-1 as evidence to assist personalized precision treatment. Methods Forty-six patients with PDAC (31 in the primary cohort and 15 in the validation cohort) who underwent curative resection and subsequently adjuvant chemotherapy with S-1 were included. Pre-operative abdominal contrast-enhanced MRI was performed, and radiomics features of the whole PDAC were extracted from the primary cohort. After univariable analysis and radiomics features selection, a multivariable Cox regression model for survival analysis was subsequently used to select statistically significant factors associated with postoperative disease-free survival (DFS). Predictive capacities of the factors were tested on the validation cohort by using Kaplan–Meier method. Results Multivariable Cox regression analysis identified the probability of T1WI_NGTDM_Strength and tumour location as independent predictors of the efficacy of S-1 for adjuvant chemotherapy of PDAC (p = 0.005 and 0.013) in the primary cohort, with hazard ratios (HRs) of 0.289 and 0.293, respectively. Further survival analysis showed that patients in the low-T1WI_NGTDM_Strength group had shorter DFS (median = 5.1 m) than those in the high-T1WI_NGTDM_Strength group (median = 13.0 m) (p = 0.006), and patients with PDAC on the pancreatic head exhibited shorter DFS (median = 7.0 m) than patients with tumours in other locations (median = 20.0 m) (p = 0.016). In the validation cohort, the difference in DFS between patients with low-T1WI_NGTDM_Strength and high-T1WI_NGTDM_Strength and the difference between patients with PDAC on the pancreatic head and that in other locations were approved, with marginally significant (p = 0.073 and 0.050), respectively. Conclusions Whole-tumour radiomics feature of T1WI_NGTDM_Strength and tumour location were potential predictors of the efficacy of S-1 and for the precision selection of S-1 as adjuvant chemotherapy regimen for PDAC.

Background: Late gadolinium enhancement (LGE) derived from cardiac magnetic resonance (CMR) represents myocardial fibrosis (MF) and is associated with prognosis in hypertrophic cardiomyopathy (HCM). However, it cannot be evaluated when CMR is unavailable. Hence, we aimed to investigate the ability of radiomic features derived from coronary computed tomography angiography (CCTA) to detect the presence and extent of MF in HCM, with LGE as references. Methods: 161 patients with HCM who underwent CCTA and CMR were retrospectively enrolled and randomly divided into training (107 patients, 1712 segments) and testing cohorts (54 patients, 864 segments). Segments were obtained according to AHA 17-segment method. Radiomic features were extracted from per-segment and entire myocardium regions, and multiple machine-learning algorithms were used for radiomic signatures (Rad-sig) generation and model building. Four models were established by multivariable logistic regression using Rad-sig (R-model), clinical characteristic (C-model), echocardiography parameters (E-model), and all features integrated (Integ-model) to identify LGE/left ventricular mass >_ 15%. Results: The model achieved good diagnostic accuracy in both training (area under the curve [AUC]:0.81, 95% confidence interval [CI]: 0.78-0.83) and testing cohort (AUC: 0.78, 95%CI: 0.75-0.81) on a per-segment basis for the presence of MF. The Integ-model owned the highest discriminative ability for patients with LGE/left ventricular mass >_ 15% in both training and testing cohorts with AUC of 0.94 (95%CI: 0.89-0.98) and 0.92 (95%CI: 0.85-0.99), respectively. Conclusions: Our radiomic models were considered as useful and complementary biomarkers for the evaluation of the presence and extent of MF on CCTA, facilitating clinical decision-making and risk stratification in HCM patients. (c) 2021 Elsevier B.V. All rights reserved.