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Background: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR). Methods: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 − , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively. Conclusions: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi‐parametric magnetic resonance imaging (mp‐MRI) radiomics in GC is less clear. Purpose: To compare and combine CT and mp‐MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC. Study Type: Retrospective. Population: Two hundred twenty‐five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly. Field/Sequence: T2‐weighted fast spin echo (fat suppressed T2‐weighted imaging [fs‐T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T. Assessment: Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs‐T2WI, ADC, DCE, and mp‐MRI Radiomics score (Radscores) were compared between responders and non‐responders. A multimodal nomogram combining CT and mp‐MRI Radscores was developed. Patients were followed up for 3–65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated. Statistical Tests: A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan–Meier survival analysis and C‐index. A P value <0.05 was considered statistically significant. Results: CT Radscore, mp‐MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834–0.937) and 0.871 (0.767–0.940) in training and validation datasets, respectively. The C‐index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp‐MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137). Data Conclusions: mp‐MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability. Evidence Level: 3 Technical Efficacy: 2 [ABSTRACT FROM AUTHOR]

Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-910/coif). The authors have no conflicts of interest to declare.

Objective: The objective of our study was to retrospectively evaluate the efficacy of combined analysis of T2-weighted imaging and DWI in the diagnosis of parametrial invasion (PMI) in cervical carcinoma., Materials and Methods: The clinical records of 192 patients with cervical carcinoma who met the study requirements were reviewed for this retrospective study. The signal intensities of suspicious PMI tissue were assessed on T2-weighted images, DW images, and apparent diffusion coefficient maps independently by two experienced radiologists. The radiologist observers predicted the presence of PMI by scoring T2-weighted imaging alone and then by scoring T2-weighted imaging and DWI combined. The results were compared with histopathologic findings., Results: Histopathologic findings revealed PMI in 24 of 192 study subjects. In positively predicting the presence of PMI, T2-weighted imaging and DWI combined scored significantly better than T2-weighted imaging alone, as proven by high sensitivity (T2-weighted imaging alone vs T2-weighted imaging and DWI combined: observer 1, 75.0% vs 83.3% [p = 0.477]; observer 2, 66.7% vs 91.7% [p < 0.05]), high specificity (T2-weighted imaging alone vs T2-weighted imaging and DWI combined: observer 1, 84.5% vs 98.8% [p < 0.001]; observer 2, 85.7% vs 98.8% [p < 0.001]), and high accuracy (T2-weighted imaging alone vs T2-weighted imaging and DWI combined: observer 1, 83.3% vs 96.9% [p < 0.001]; observer 2, 83.3% vs 97.9% [p < 0.001]). The area under the ROC curve was also significantly higher for T2-weighted imaging and DWI combined (observer 1, 0.911; observer 2, 0.952) than for T2-weighted imaging alone (observer 1, 0.798; observer 2, 0.762). Although the interobserver agreement was good for T2-weighted imaging (κ = 0.695) and excellent for T2-weighted imaging and DWI combined (κ = 0.753), the improvement failed to achieve statistical significance (p = 0.28)., Conclusion: Combined analysis of T2-weighted imaging and DWI enhances the accuracy of diagnosing PMI in patients with cervical cancer compared with T2-weighted imaging alone.