Deep learning models for histologic grading of breast cancer and association with disease prognosis.

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.

Determining breast cancer biomarker status and associated morphological features using deep learning.

Background: Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results. Methods: We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three data sources. Areas under the receiver operator characteristic curve (AUCs) were calculated for test sets at both a patch-level (>135 million patches, 181 slides) and slide-level (n = 3274 slides, 1249 cases, 37 sites). Interpretability analyses were performed using Testing with Concept Activation Vectors (TCAV), saliency analysis, and pathologist review of clustered patches. Results: The patch-level AUCs are 0.939 (95%CI 0.936-0.941), 0.938 (0.936-0.940), and 0.808 (0.802-0.813) for ER/PR/HER2, respectively. At the slide level, AUCs are 0.86 (95%CI 0.84-0.87), 0.75 (0.73-0.77), and 0.60 (0.56-0.64) for ER/PR/HER2, respectively. Interpretability analyses show known biomarker-histomorphology associations including associations of low-grade and lobular histology with ER/PR positivity, and increased inflammatory infiltrates with triple-negative staining. Conclusions: This study presents rapid breast cancer biomarker estimation from routine H&E slides and builds on prior advances by prioritizing interpretability of computationally learned features in the context of existing pathological knowledge.

Erratum: Publisher Correction: Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.

[This corrects the article DOI: 10.1038/s41746-019-0112-2.].

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p = 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

Critical Evaluation of Risk Factors and Early Complications in 564 Consecutive Two-Stage Implant-Based Breast Reconstructions Using Acellular Dermal Matrix at a Single Center.

BACKGROUND: Acellular dermal matrix for implant-based breast reconstruction appears to cause higher early complication rates, but long-term outcomes are perceived to be superior. This dichotomy is the subject of considerable debate. The authors hypothesized that patient characteristics and operative variables would have a greater impact on complications than the type of acellular dermal matrix used. METHODS: A retrospective cohort study was performed of consecutive patients who underwent two-stage, implant-based breast reconstruction with human cadaveric or bovine acellular dermal matrix from 2006 to 2012 at a single institution. Patient characteristics and operative variables were analyzed using logistic regression analyses to identify risk factors for complications. RESULTS: The authors included 564 reconstructions in the study. Radiation therapy and obesity increased the odds of all complications. Every 100-ml increase in preoperative breast volume increased the odds of any complication by 1 percent, the odds of infection by 27 percent, and the risk of explantation by 16 percent. The odds of seroma increased linearly with increasing surface area of acellular dermal matrix. Odds of infection were higher with an intraoperative expander fill volume greater than 50 percent of the total volume. Risk of explantation was twice as high when intraoperative expander fill volume was greater than 300 ml. CONCLUSIONS: Radiation therapy, obesity, larger breasts, higher intraoperative fill volumes, and larger acellular dermal matrices are all independent risk factors for early complications. Maximizing the initial mastectomy skin envelope fill must be balanced with the understanding that higher complication rates may result from a larger intraoperative breast mound. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.