The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast 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.

Despite the advantages of providing an early presumptive diagnosis, fungal classification by histopathology can be difficult and may lead to diagnostic error. To assess the accuracy of histologic diagnosis of fungal infections vs culture ("gold standard"), we performed a 10-year retrospective review at our institution. Of the 47 of 338 positive mold and yeast cultures with concurrent surgical pathology evaluation without known history of a fungal infection, 37 (79%) were correctly identified based on morphologic features in histologic and/or cytologic specimens. The 10 discrepant diagnoses (21%) included misidentification of septate and nonseptate hyphal organisms and yeast forms. Errors resulted from morphologic mimics, use of inappropriate terminology, and incomplete knowledge in mycology. The accuracy did not correlate with preceding antifungal therapy (P = .14) or use of special stains (P = .34) and was not operator-dependent. Among 8 discrepancies with clinical follow-up available, 2 potential adverse clinical consequences resulted. While histopathologic identification of fungi in tissue sections and cytologic preparations is prone to error, implementation of a standardized reporting format should improve diagnostic accuracy and prevent adverse outcomes.

Aim To describe a group of distinct low‐grade oncocytic renal tumours that demonstrate CD117 negative/cytokeratin (CK) 7‐positive immunoprofile. Methods and results We identified 28 such tumours from four large renal tumour archives. We performed immunohistochemistry for: CK7, CD117, PAX8, CD10, AMACR, e‐cadherin, CK20, CA9, AE1/AE3, vimentin, BerEP4, MOC31, CK5/6, p63, HMB45, melan A, CD15 and FH. In 14 cases we performed array CGH, with a successful result in nine cases. Median patient age was 66 years (range 49–78 years) with a male‐to‐female ratio of 1:1.8. Median tumour size was 3 cm (range 1.1–13.5 cm). All were single tumours, solid and tan‐brown, without a syndromic association. On microscopy, all cases showed solid and compact nested growth. There were frequent areas of oedematous stroma with loosely arranged cells. The tumour cells had oncocytic cytoplasm with uniformly round to oval nuclei, but without significant irregularities, and showed only focal perinuclear halos. Negative CD117 and positive CK7 reactivity were present in all cases (in two cases there was focal and very weak CD117 reactivity). Uniform reactivity was found for PAX8, AE1/AE3, e‐cadherin, BerEP4 and MOC31. Negative stains included CA9, CK20, vimentin, CK5/6, p63, HMB45, Melan A and CD15. CD10 and AMACR were either negative or focally positive; FH was retained. On array CGH, there were frequent deletions at 19p13.3 (seven of nine), 1p36.33 (five of nine) and 19q13.11 (four of nine); disomic status was found in two of nine cases. On follow‐up (mean 31.8 months, range 1–118), all patients were alive with no disease progression. Conclusion Low‐grade oncocytic tumours that are CD117‐negative/CK7‐positive demonstrate consistent and readily recognisable morphology, immunoprofile and indolent behaviour.