High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). We hypothesize that integrating high sTILs and additional clinicopathologic features associated with pCR could enhance our ability to predict the group of patients on whom treatment de-escalation strategies could be tested. In this prospective early-stage TNBC neoadjuvant chemotherapy study, pretreatment biopsies from 408 patients were evaluated for their clinical and demographic features, as well as biomarkers including sTILs, Ki-67, PD-L1 and androgen receptor. Multivariate logistic regression models were developed to generate a computed response score to predict pCR. The pCR rate for the entire cohort was 41%. Recursive partitioning analysis identified ≥20% as the optimal cutoff for sTILs to denote 35% (143/408) of patients as having high sTILs, with a pCR rate of 59%, and 65% (265/408) of patients as having low sTILs, with a pCR rate of 31%. High Ki-67 (cutoff > 35%) was identified as the only predictor of pCR in addition to sTILs in the training set. This finding was verified in the testing set, where the highest computed response score encompassing both high sTILa and high Ki-67 predicted a pCR rate of 65%. Integrating Ki67 and sTIL may refine the selection of early stage TNBC patients for neoadjuvant clinical trials evaluating de-escalation strategies.

BACKGROUND AND PURPOSE: Minimally invasive parathyroid surgery relies critically on image guidance, but data comparing the efficacy of various imaging modalities are scarce. Our aim was to perform a blinded comparison of the localizing capability of technetium Tc99m sestamibi SPECT, multiphase multidetector 4D CT, and the combination of these 2 modalities (technetium Tc99m sestamibi SPECT + multiphase multidetector 4D CT). MATERIALS AND METHODS: We reviewed the records of 31 (6 men, 25 women; median age, 56 years) consecutive patients diagnosed with biochemically confirmed primary hyperparathyroidism between November 2009 and March 2010 who underwent preoperative technetium Tc99m sestamibi SPECT and multiphase multidetector 4D CT performed on the same scanner with pathologic confirmation by resection of a single parathyroid adenoma. Accuracy was determined separately for localization to the correct side and quadrant using surgical localization as the standard of reference. RESULTS: Surgical resection identified 14 left and 17 right parathyroid adenomas and 2 left inferior, 12 left superior, 11 right inferior, and 6 right superior parathyroid adenomas. For left/right localization, technetium Tc99m sestamibi SPECT achieved an accuracy of 93.5% (29 of 31), multiphase multidetector 4D CT achieved 96.8% accuracy (30 of 31), and technetium Tc99m sestamibi SPECT + multiphase multidetector 4D CT achieved 96.8% accuracy (30 of 31). For quadrant localization, technetium Tc99m sestamibi SPECT accuracy was 67.7% (21 of 31), multiphase multidetector 4D CT accuracy was 87.1% (27 of 31), and technetium Tc99m sestamibi SPECT + multiphase multidetector 4D CT accuracy was 93.5% (29 of 31). Reader diagnostic confidence was consistently ranked lowest for technetium Tc99m sestamibi SPECT and highest for technetium Tc99m sestamibi SPECT + multiphase multidetector 4D CT. CONCLUSIONS: For left/right localization of parathyroid adenomas, all modalities performed equivalently. For quadrant localization, technetium Tc99m sestamibi SPECT + multiphase multidetector 4D CT is superior to technetium Tc99m sestamibi SPECT.

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients’ treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.