CT Imaging Findings in Patients with Ovarian Cancer and Acute Abdominal Symptoms: Experience at a Tertiary Cancer Center.

PURPOSE: To evaluate computed tomography (CT) findings in patients with ovarian cancer presenting to a comprehensive cancer center's urgent care unit with acute abdominal symptoms. METHODS: This retrospective study included consecutive patients with ovarian cancer who underwent abdominal CT at a comprehensive cancer center's urgent care unit between January 1, 2018, and January 14, 2020, due to acute abdominal symptoms. Two abdominal radiologists reviewed the abdominal CT reports, categorizing imaging findings as follows: (a) no new or acute finding, (b) new or increased bowel or gastric obstruction, (c) new or increased ascites, (d) new or increased peritoneal carcinomatosis, (e) new or increased nonperitoneal metastases, (f) new inflammatory or infectious changes, (g) new or increased hydronephrosis, (h) new or increased biliary dilatation, (i) new vascular complications, or (j) new bowel perforation. RESULTS: A total of 200 patients (mean age, 59 years; range, 22-87) underwent a total of 259 abdominal CT scans, of which 217/259 (83.8%) scans were found to have new or increased findings. A total of 115/259 (44.4%) scans had only one finding while 102/259 (39.4%) scans had 2 or more findings. Altogether, 382 new or increased findings were detected: findings were most commonly related to bowel or gastric obstruction (92/382, 24.1%) with small bowel obstruction being the most common finding (80/382, 20.9%); ascites (78/382, 20.4%); peritoneal carcinomatosis (62/382, 16.2%); and nonperitoneal metastases (62/382, 16.2%). Inflammatory or infectious findings accounted for 30/382 (7.9%) findings. CONCLUSION: Most patients with ovarian cancer presenting with acute abdominal had relevant positive findings on abdominal CT, with small bowel obstruction being the most common finding.

Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports.

The development of digital cancer twins relies on the capture of high-resolution representations of individual cancer patients throughout the course of their treatment. Our research aims to improve the detection of metastatic disease over time from structured radiology reports by exposing prediction models to historical information. We demonstrate that Natural language processing (NLP) can generate better weak labels for semi-supervised classification of computed tomography (CT) reports when it is exposed to consecutive reports through a patient's treatment history. Around 714,454 structured radiology reports from Memorial Sloan Kettering Cancer Center adhering to a standardized departmental structured template were used for model development with a subset of the reports included for validation. To develop the models, a subset of the reports was curated for ground-truth: 7,732 total reports in the lung metastases dataset from 867 individual patients; 2,777 reports in the liver metastases dataset from 315 patients; and 4,107 reports in the adrenal metastases dataset from 404 patients. We use NLP to extract and encode important features from the structured text reports, which are then used to develop, train, and validate models. Three models-a simple convolutional neural network (CNN), a CNN augmented with an attention layer, and a recurrent neural network (RNN)-were developed to classify the type of metastatic disease and validated against the ground truth labels. The models use features from consecutive structured text radiology reports of a patient to predict the presence of metastatic disease in the reports. A single-report model, previously developed to analyze one report instead of multiple past reports, is included and the results from all four models are compared based on accuracy, precision, recall, and F1-score. The best model is used to label all 714,454 reports to generate metastases maps. Our results suggest that NLP models can extract cancer progression patterns from multiple consecutive reports and predict the presence of metastatic disease in multiple organs with higher performance when compared with a single-report-based prediction. It demonstrates a promising automated approach to label large numbers of radiology reports without involving human experts in a time- and cost-effective manner and enables tracking of cancer progression over time.

The impact of body composition parameters on ipilimumab toxicity and survival in patients with metastatic melanoma.

BACKGROUND: Body composition is an important predictor of drug toxicity and outcome. Ipilimumab (Ipi), a monoclonal antibody used to treat metastatic melanoma, has specific toxicities. No validated biomarkers that predict Ipi toxicity and efficacy exist. Also, the impact of Ipi on body composition has not been established. METHODS: Patients with metastatic melanoma treated with Ipi between 2009 and 2015 were included. Body composition was assessed by computed tomography at baseline and after four cycles of Ipi. Sarcopenia and low muscle attenuation (MA) were defined using published cut-points. All adverse events (AEs) and immune-related AEs (irAEs) were recorded (Common Terminology Criteria For Adverse Event V.4.0). RESULTS: Eighty-four patients were included in this study (62% male, median age 54 years). At baseline, 24% were sarcopenic and 33% had low MA. On multivariate analysis, sarcopenia and low MA were significantly associated with high-grade AEs (OR=5.34, 95% CI: 1.15-24.88, P=0.033; OR=5.23, 95% CI: 1.41-19.30, P=0.013, respectively), and low MA was associated with high-grade irAEs (OR=3.57, 95% CI: 1.09-11.77, P=0.036). Longitudinal analysis (n=59) revealed significant reductions in skeletal muscle area (SMA), total body fat-free mass, fat mass (all P<0.001) and MA (P=0.030). Mean reduction in SMA was 3.3%/100 days (95% CI: -4.48 to -1.79%, P<0.001). A loss of SMA ⩾7.5%/100 days (highest quartile) was a significant predictor of overall survival in multivariable Cox regression analysis (HR: 2.1, 95% CI: 1.02-4.56, P=0.046). CONCLUSIONS: Patients with sarcopenia and low MA are more likely to experience severe treatment-related toxicity to Ipi. Loss of muscle during treatment was predictive of worse survival. Treatments to increase muscle mass and influence outcome warrant further investigation.