Advancing Clinicopathologic Diagnosis of High-risk Neuroblastoma Using Computerized Image Analysis and Proteomic Profiling

Multifocal primary neuroblastoma tumor heterogeneity in siblings with co-occurring PHOX2B and NF1 genetic aberrations

Neuroblastoma, the most common extracranial solid tumor of childhood, can present in multiple primary sites, but the extent of genetic heterogeneity among tumor foci, as well as the presence or absence of common oncogenic drivers, remains unknown. Although PHOX2B genetic aberrations can cause familial neuroblastoma, they demonstrate incomplete penetrance with respect to neuroblastoma pathogenesis, suggesting that additional undescribed oncogenic drivers are necessary for tumor development. We performed comprehensive molecular characterization of neuroblastoma tumors from two siblings affected by familial multifocal neuroblastoma, including whole exome sequencing and single-nucleotide polymorphism (SNP) arrays of tumor and matched blood samples. Data were processed and analyzed using established bioinformatics algorithms to evaluate for germline and somatic mutations and copy number variations (CNVs). We confirmed the presence of a PHOX2B deletion and NF1 mutation across all tumor samples and the germline genome. Matched tumor-blood whole exome sequencing also identified 365 genes that contained nonsilent coding mutations across all tumor samples, with no recurrent mutations across all tumors. SNP arrays also showed significant heterogeneity with respect to CNVs. The only common CNV across all tumors was 17q gain, with differing chromosomal coordinates across samples but a common region of overlap distal to 17q21.31, suggesting this adverse prognostic biomarker may offer insight about additional drivers for multifocal neuroblastoma in patients with germline PHOX2B or NF1 aberrations. Molecular characterization of all tumors from patients with multifocal primary neuroblastoma has potential to yield novel insights on neuroblastoma pathogenesis.

Digital pathology and artificial intelligence

In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.

Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer

Histopathologic features, particularly Gleason grading system, have contributed significantly to the diagnosis, treatment, and prognosis of prostate cancer for decades. However, prostate cancer demonstrates enormous heterogeneity in biological behavior, thus establishing improved prognostic and predictive markers is particularly important to personalize therapy of men with clinically localized and newly diagnosed malignancy. Many automated grading systems have been developed for Gleason grading but acceptance in the medical community has been lacking due to poor interpretability. To overcome this problem, we developed a set of visually meaningful features to differentiate between low- and high-grade prostate cancer. The visually meaningful feature set consists of luminal and architectural features. For luminal features, we compute: 1) the shortest path from the nuclei to their closest luminal spaces; 2) ratio of the epithelial nuclei to the total number of nuclei. A nucleus is considered an epithelial nucleus if the shortest path between it and the luminal space does not contain any other nucleus; 3) average shortest distance of all nuclei to their closest luminal spaces. For architectural features, we compute directional changes in stroma and nuclei using directional filter banks. These features are utilized to create two subspaces; one for prostate images histopathologically assessed as low grade and the other for high grade. The grade associated with a subspace, which results in the minimum reconstruction error is considered as the prediction for the test image. For training, we utilized 43 regions of interest (ROI) images, which were extracted from 25 prostate whole slide images of The Cancer Genome Atlas (TCGA) database. For testing, we utilized an independent dataset of 88 ROIs extracted from 30 prostate whole slide images. The method resulted in 93.0% and 97.6% training and testing accuracies, respectively, for the spectrum of cases considered. The application of visually meaningful features provided promising levels of accuracy and consistency for grading prostate cancer.