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Background: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achieving a pathologic complete response (PCR) after NAC. Our study aims to develop 2 multimodal ultrasound deep learning (DL) models to noninvasively predict resistance and PCR to NAC before treatment., Methods: From January 2017 to July 2022, a total of 170 patients with breast cancer were prospectively enrolled. All patients underwent multimodal ultrasound examination (grayscale 2D ultrasound and ultrasound elastography) before NAC. We combined clinicopathological information to develop 2 DL models, DL_Clinical_resistance and DL_Clinical_PCR, for predicting resistance and PCR to NAC, respectively. In addition, these 2 models were combined to stratify the prediction of response to NAC., Results: In the test cohort, DL_Clinical_resistance had an AUC of 0.911 (95%CI, 0.814-0.979) with a sensitivity of 0.905 (95%CI, 0.765-1.000) and an NPV of 0.882 (95%CI, 0.708-1.000). Meanwhile, DL_Clinical_PCR achieved an AUC of 0.880 (95%CI, 0.751-0.973) and sensitivity and NPV of 0.875 (95%CI, 0.688-1.000) and 0.895 (95%CI, 0.739-1.000), respectively. By combining DL_Clinical_resistance and DL_Clinical_PCR, 37.1% of patients with resistance and 25.7% of patients with PCR were successfully identified by the combined model, suggesting that these patients could benefit by an early change of treatment strategy or by implementing an organ preservation strategy after NAC., Conclusions: The proposed DL_Clinical_resistance and DL_Clinical_PCR models and combined strategy have the potential to predict resistance and PCR to NAC before treatment and allow stratified prediction of NAC response., (© The Author(s) 2023. Published by Oxford University Press.)

Background: Endosonographers are highly dependent on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). The objectives of this study were to develop a deep-learning radiomics (DLR) model based on endoscopic ultrasonography (EUS) images for identifying PDAC and to explore its true clinical benefit., Methods: A retrospective data set of EUS images that included PDAC and benign lesions was used as a training cohort (N = 368 patients) to develop the DLR model, and a prospective data set was used as a test cohort (N = 123 patients) to validate the effectiveness of the DLR model. In addition, seven endosonographers performed two rounds of reader studies on the test cohort with or without DLR assistance to further assess the clinical applicability and true benefits of the DLR model., Results: In the prospective test cohort, DLR exhibited an area under the receiver operating characteristic curves of 0.936 (95% confidence interval [CI], 0.889-0.976) with a sensitivity of 0.831 (95% CI, 0.746-0.913) and 0.904 (95% CI, 0.820-0.980), respectively. With DLR assistance, the overall diagnostic performance of the seven endosonographers improved: one endosonographer achieved a significant expansion of specificity (p = .035,) and another achieved a significant increase in sensitivity (p = .038). In the junior endosonographer group, the diagnostic performance with the help of the DLR was higher than or comparable to that of the senior endosonographer group without DLR assistance., Conclusions: A prospective test cohort validated that the DLR model based on EUS images effectively identified PDAC. With the assistance of this model, the gap between endosonographers at different levels of experience narrowed, and the accuracy of endosonographers expanded., (© 2023 American Cancer Society.)

Objectives: Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage., Methods: In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration., Results: In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC., Conclusions: The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients., Key Points: • We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options., (© 2021. European Society of Radiology.)