A systems approach to clinical oncology uses deep phenotyping to deliver personalized care | Nature Reviews Clinical Oncology
Cancer encompasses a complex, heterogeneous and dynamic group of diseases that arise from perturbations to multiple biological networks within the body. A systems biology-based approach would help to decipher this complexity, to deeply characterize the pathophysiology of the disease and to stratify cancers into appropriate molecular subtypes to facilitate the development of personalized therapies. Technological advances made over the past decade have enabled multiscale, longitudinal measurements (‘snapshots’) of human biology, from single-cell analyses to whole-body monitoring. In this Perspective, the authors discuss some of these technologies and how they have (and will) contributed to our understanding of cancer biology as well as to the development of early diagnostics and personalized therapies.
Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method | The Lancet Digital Health
Current lung cancer screening guidelines use either mean diameter, volume, or density of the largest lung nodule on the previous CT scan or appearance of a new nodule to ascertain the timing of the next CT scan. Researchers aimed to develop an accurate screening protocol by estimating the 3-year lung cancer risk after two screening CT scans using deep learning of radiologists’ CT readings and other universally available clinical information. A deep learning algorithm (referred to as DeepLR) was developed using data from participants who had received at least two CT screening scans up to 2 years apart in the National Lung Screening Trial (NLST; training cohort). Double-blinded validation was done using data from participants in the Pan-Canadian Early Detection of Lung Cancer (PanCan) study (validation cohort).
Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports | JAMA Oncology
Can deep natural language processing of radiologic reports be used to measure real-world oncologic outcomes, including disease progression and response to therapy? In a cohort study of 2406 patients with lung cancer, the findings suggested that deep learning models may estimate human curations of the presence of active cancer, cancer worsening/progression, and cancer improvement/response in radiologic reports with good discrimination (area under the receiver operating characteristic curve, >0.90). Statistically significant associations between these end points and overall survival were observed.