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BACKGROUND A subset of patients treated with immune checkpoint inhibitors experience an accelerated tumor growth rate (TGR) in comparison with pretreatment kinetics; this is known as hyperprogression. This study assessed the relation between hyperprogressive disease (HPD) and treatment-related toxicity and clinical factors. METHODS This study reviewed patients with solid tumors who were enrolled in early-phase immunotherapy trials at Princess Margaret Cancer Centre between August 2012 and September 2016 and had computed tomography scans in the pre-immunotherapy (reference) and on-immunotherapy (experimental) periods. HPD was defined as progression according to Response Evaluation Criteria in Solid Tumors 1.1 at the first on-treatment scan and a ≥2-fold increase in TGR between the reference and experimental periods. Treatment-related toxicities requiring systemic therapy, drug delays, or discontinuation were considered clinically significant adverse events (CSAEs). RESULTS Of 352 patients, 182 were eligible for analysis. The median age was 60 years, and 54% were male. The Eastern Cooperative Oncology Group performance status was 0 (32%) or 1 (68%). The Royal Marsden Hospital (RMH) prognostic score was 0/1 in 59%. Single-agent immunotherapy was given to 80% of the patients. Most patients (89%) received anti-programmed death (ligand) 1 antibodies alone or in combination with other therapies. HPD occurred in 12 of 182 patients (7%). A higher proportion of females was seen among HPD patients (P = .01), but no association with age, performance status, tumor type, RMH prognostic score, combination immunotherapy, or CSAEs was found. The 1-year overall survival rate was 28% for HPD patients and 53% for non-HPD patients (hazard ratio, 1.7; 95% confidence interval, 0.9-3.3; P = .11). CONCLUSIONS HPD was observed in 7% of patients with solid tumors treated with immunotherapy. HPD was not associated with CSAEs, age, tumor type, or the type of immunotherapy but was more common in females.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70–75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80–98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.

The field of radiomics is at the forefront of personalized medicine. However, there is concern that high variation in imaging parameters will impact robustness of radiomic features and subsequently the performance of the predictive models built upon them. Therefore, our review aims to evaluate the impact of imaging parameters on the robustness of radiomic features. We also provide insights into the validity and discrepancy of different methodologies applied to investigate the robustness of radiomic features. We selected 47 papers based on our predefined inclusion criteria and grouped these papers by the imaging parameter under investigation: (i) scanner parameters, (ii) acquisition parameters and (iii) reconstruction parameters. Our review highlighted that most of the imaging parameters are disruptive parameters, and shape along with First order statistics were reported as the most robust radiomic features against variation in imaging parameters. This review identified inconsistencies related to the methodology of the reviewed studies such as the metrics used for robustness, the feature extraction techniques, the reporting style, and their outcome inclusion. We hope this review will aid the scientific community in conducting research in a way that is more reproducible and avoids the pitfalls of previous analyses.

Background: Radiomic features in pancreatic ductal adenocarcinoma (PDAC) often lack validation in independent test sets or are limited to early or late stage disease. Given the lethal nature of PDAC it is possible that there are similarities in radiomic features of both early and advanced disease reflective of aggressive biology. Purpose: To assess the performance of prognostic radiomic features previously published in patients with resectable PDAC in a test set of patients with unresectable PDAC undergoing chemotherapy. Methods: The pre-treatment CT of 108 patients enrolled in a prospective chemotherapy trial were used as a test cohort for 2 previously published prognostic radiomic features in resectable PDAC (Sum Entropy and Cluster Tendency with square-root filter[Sqrt]). We assessed the performance of these 2 radiomic features for the prediction of overall survival (OS) and time to progression (TTP) using Cox proportional-hazard models. Results: Sqrt Cluster Tendency was significantly associated with outcome with a hazard ratio (HR) of 1.27(for primary pancreatic tumor plus local nodes), (Confidence Interval(CI):1.01 -1.6, P-value = 0.039) for OS and a HR of 1.25(CI:1.00 -1.55, P-value = 0.047) for TTP. Sum entropy was not associated with outcomes. Sqrt Cluster Tendency remained significant in multivariate analysis. Conclusion: The CT radiomic feature Sqrt Cluster Tendency, previously demonstrated to be prognostic in resectable PDAC, remained a significant prognostic factor for OS and TTP in a test set of unresectable PDAC patients. This radiomic feature warrants further investigation to understand its biologic correlates and CT applicability in PDAC patients.

The field of radiomics is at the forefront of personalized medicine. However, there are concerns regarding the robustness of its features against multiple medical imaging parameters and the performance of the predictive models built upon them. Therefore, our review aims to identify image perturbation factors (IPF) that most influence the robustness of radiomic features in biomedical research. We also provide insights into the validity and discrepancy of different methodologies applied to investigate the robustness of radiomic features. We selected 527 papers based on the primary criterion that the papers had imaging parameters that affected the reproducibility of radiomic features extracted from computed tomography (CT) images. We compared the reported performance of these parameters along with IPF in the eligible studies. We then proceeded to divide our studies into three groups based on the type of their IPF: (i) scanner parameters, (ii) acquisition parameters and (iii) reconstruction parameters. Our review highlighted that the reconstruction algorithm was the most reproducible factor and shape along with intensity histogram (IH) were the most robust radiomic features against variation in imaging parameters. This review identified substantial inconsistencies related to the methodology and the reporting style of the reviewed studies such as type of study performed, the metrics used for robustness, the feature extraction techniques, the image perturbation factors, the reporting style and their outcome inclusion. Finally, we hope the IPFs and the methodology inconsistencies identified will aid the scientific community in conducting research in a way that is more reproducible and avoids the pitfalls of previous analyses.