Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance. METHODS: In total, 540 breast cancer patients receiving standard NAC were enrolled. Based on H&E-stained images, DL methods were employed to automatically identify tumor epithelium and predict pCR by scoring the identified tumor epithelium to produce a histopathological biomarker, the pCR-score. The predictive performance of the pCR-score was assessed and compared with that of conventional biomarkers including stromal tumor-infiltrating lymphocytes (sTILs) and subtype. RESULTS: The pCR-score derived from H&E staining achieved an area under the curve (AUC) of 0.847 in predicting pCR directly, and achieved accuracy, F1 score, and AUC of 0.853, 0.503, and 0.822 processed by the logistic regression method, respectively, higher than either sTILs or subtype; a prediction model of pCR constructed by integrating sTILs, subtype and pCR-score yielded a mean AUC of 0.890, outperforming the baseline sTIL-subtype model by 0.051 (0.839, P = 0.001). CONCLUSION: The DL-based pCR-score from histological images is predictive of pCR better than sTILs and subtype, and holds the great potentials for a more accurate stratification of patients for NAC.

Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer.

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even obtain worse outcomes after therapy. Hence, predictors of treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potential of breast cancer stromal histology via a deep learning (DL)-based approach and proposed the tumor-associated stroma score (TS-score) for predicting pathological complete response (pCR) to NAC with a multicenter dataset. The TS-score was demonstrated to be an independent predictor of pCR, and it not only outperformed the baseline variables and stromal tumor-infiltrating lymphocytes (sTILs) but also significantly improved the prediction performance of the baseline variable-based model. Furthermore, we discovered that unlike lymphocytes, collagen and fibroblasts in the stroma were likely associated with a poor response to NAC. The TS-score has the potential to better stratify breast cancer patients in NAC settings.

Uncommon primary Group A streptococcal erector spinae pyomyositis diagnosed as muscle strain in a fit young male.

Acute lower back pain in a fit healthy male with recent strenuous physical activity is often attributed to muscle strain. We report a rare case of erector spinae pyomyositis developing in a young and otherwise healthy young adult male that was almost misdiagnosed as muscle strain. Despite admission and close monitoring, diagnosis was only confirmed on a magnetic resonance imaging of the spine later during the hospital stay. Early diagnosis in this case allowed successful treatment with intravenous antibiotics alone, without requiring further surgical drainage or development of further neurological sequelae, common in later stages of erector spinae pyomyositis.

[Progression in diagnostic pathology; development of virtual microscopy and its applications].

Many systems have already been designed and successfully used for clinical laboratory and pathological examination. The evolution of image analysis was enabled when analog images of the original glass slides could be transferred to digital images with the rapid development of virtual microscopy and virtual slides depended upon computer technologies. Today, whole slide can be acquired by virtual microscopes. The applications of virtual microscopy and virtual slides for teaching, diagnosis, telepathology, and research are more widely used than those of real microscope and real glass slides. In traditional cancer diagnosis, pathologists examine biopsies to make diagnostic assessments largely based on two-dimensional cell morphology and tissue distribution. These assessments are subjective and often show considerable variability. However, automated cancer diagnostic system based on three-dimensional image analysis based on nuclear bulging sign enables objective judgments using quantitative measurements. We expect that the shortage of pathologists will be improved when an automated cancer diagnosis system is developed.