Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features.

Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the primary site of origin (PSO) of a metastasis can help in guiding therapeutic options for liver metastases. In this pilot study, we hypothesized that computer extracted handcrafted (HC) histomorphometric features can be utilized to identify the PSO of liver metastases. Cellular features, including tumor nuclei morphological and graph features as well as cytoplasm texture features, were extracted by computer algorithms from 175 slides (114 patients). The study comprised three experiments: (1) comparing and (2) fusing a machine learning (ML) model trained with HC pathomic features and deep learning (DL)-based classifiers to predict site of origin; (3) identifying the section of the primary tumor from which metastases were derived. For experiment 1, we divided the cohort into training sets composed of primary and matched liver metastases [60 patients, 121 whole slide images (WSIs)], and a hold-out validation set (54 patients, 54 WSIs) composed solely of liver metastases of known site of origin. Using the extracted HC features of the training set, a combination of supervised machine classifiers and unsupervised clustering was applied to identify the PSO. A random forest classifier achieved areas under the curve (AUCs) of 0.83, 0.64, 0.82, and 0.64 in classifying the metastatic tumor from colon, esophagus, breast, and pancreas on the validation set. The top features related to nuclear and peri-nuclear shape and textural attributes. We also trained a DL network to serve as a direct comparison to our method. The DL model achieved AUCs for colon: 0.94, esophagus: 0.66, breast: 0.79, and pancreas: 0.67 in identifying PSO. A decision fusion-based strategy was deployed to fuse the trained ML and DL classifiers and achieved slightly better results than ML or DL classifier alone (colon: 0.93, esophagus: 0.68, breast: 0.81, and pancreas: 0.69). For the third experiment, WSI-level attention maps were also generated using a trained DL network to generate a composite feature similarity heat map between paired primaries and their associated metastases. Our experiments revealed that epithelium-rich and moderately differentiated tumor regions of primary tumors were quantitatively similar to paired metastatic tumors. Our findings suggest that a combination of HC and DL features could potentially help identify the PSO for liver metastases while at the same time also potentially identify the spatial sites of origin for the metastases within primary tumors.

Racial and socioeconomic disparities in glioblastoma outcomes: A single-center, retrospective cohort study.

BACKGROUND: Glioblastoma (GBM) is the most common malignant primary brain tumor. Emerging reports have suggested that racial and socioeconomic disparities influence the outcomes of patients with GBM. No studies to date have investigated these disparities controlling for isocitrate dehydrogenase (IDH) mutation and O-6-methylguanine-DNA methyltransferase (MGMT) status. METHODS: Adult patients with GBM were retrospectively reviewed at a single institution from 2008 to 2019. Univariable and multivariable complete survival analyses were performed. A Cox proportional hazards model was used to assess the effect of race and socioeconomic status controlling for a priori selected variables with known relevance to survival. RESULTS: In total, 995 patients met inclusion criteria. Of these, 117 patients (11.7%) were African American (AA). The median overall survival for the entire cohort was 14.23 months. In the multivariable model, AA patients had better survival compared with White patients (hazard ratio [HR], 0.37; 95% confidence interval [CI], 0.2-0.69). The observed survival difference was significant in both a complete case analysis model and a multiple imputations model accounting for missing molecular data and controlling for treatment and socioeconomic status. AA patients with low income (HR, 2.17; 95% CI, 1.04-4.50), public insurance (HR, 2.25; 95% CI, 1.04-4.87), or no insurance (HR, 15.63; 95% CI, 2.72-89.67) had worse survival compared with White patients with low income, public insurance, or no insurance, respectively. CONCLUSIONS: Significant racial and socioeconomic disparities were identified after controlling for treatment, GBM genetic profile, and other variables associated with survival. Overall, AA patients demonstrated better survival. These findings may suggest the possibility of a protective genetic advantage in AA patients. PLAIN LANGUAGE SUMMARY: To best personalize treatment for and understand the causes of glioblastoma, racial and socioeconomic influences must be examined. The authors report their experience at the O'Neal Comprehensive Cancer Center in the deep south. In this report, contemporary molecular diagnostic data are included. The authors conclude that there are significant racial and socioeconomic disparities that influence glioblastoma outcome and that African American patients do better.