Predicting cancer content in tiles of lung squamous cell carcinoma tumours with validation against pathologist labels.

Background: A growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and cancer tissue morphology on digitized whole slide images (WSIs) of tumour resections. However, these WSIs typically contain non-cancer tissue, introducing noise during model training. As digital pathology models typically start with splitting WSIs into tiles, we propose a model that can be used to exclude non-cancer tiles from the WSIs of lung squamous cell carcinoma (SqCC) tumours.

Distinguishing recurrence from radiation-induced lung injury at the time of RECIST progressive disease on post-SABR CT scans using radiomics.

Stereotactic ablative radiotherapy (SABR) is a highly effective treatment for patients with early-stage lung cancer who are inoperable. However, SABR causes benign radiation-induced lung injury (RILI) which appears as lesion growth on follow-up CT scans. This triggers the standard definition of progressive disease, yet cancer recurrence is not usually present, and distinguishing RILI from recurrence when a lesion appears to grow in size is critical but challenging.

A Visual Aid Tool for Detection of Pancreatic Tumour-Vessel Contact on Staging CT: A Retrospective Cohort Study.

In pancreatic adenocarcinoma, the difficult distinction between normal and affected pancreas on CT studies may lead to discordance between the pre-surgical assessment of vessel involvement and intraoperative findings. We hypothesize that a visual aid tool could improve the performance of radiology residents when detecting vascular invasion in pancreatic adenocarcinoma patients. This study consisted of 94 pancreatic adenocarcinoma patient CTs.

Using deep learning to predict tumor mutational burden from scans of H&E-stained multicenter slides of lung squamous cell carcinoma.

Purpose: A high tumor mutational burden (TMB) is a promising biomarker for identifying lung squamous cell carcinoma (SqCC) patients who are more likely to benefit from risky but potentially highly beneficial immunotherapy, but it is not available in most clinics. It has been shown that it is possible to predict TMB from standard-of-care cancer histology slides using deep learning for various cancer sites. Our goal is to build a model that can do this specifically for lung SqCC and to validate it on a held-out test set from centers on which the model was not trained.

A Systematic Review Into the Radiologic Features Predicting Local Recurrence After Stereotactic Ablative Body Radiotherapy (SABR) in Patients With Non-Small Cell Lung Cancer (NSCLC).

Purpose: Posttreatment surveillance for local recurrence (LR) after stereotactic ablative body radiotherapy (SABR) can include both fluorodeoxyglucose-positron emission tomography (FDG-PET) and computed tomography (CT). Radiation-induced lung injury shares a similar appearance to LR after treatment, making the detection of LR on imaging difficult for clinicians. We aimed to summarize radiologic features of CT and FDG-PET predicting LR and to evaluate radiomics as another tool for detecting LR.