Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.

Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during therapy. Thus, there is a compelling need to identify comprehensive biomarkers that can be used longitudinally to help guide therapy choice. Herein, we report a 18F-FDG-PET/CT-based deep learning model, which demonstrates high accuracy in EGFR mutation status prediction across patient cohorts from different institutions. A deep learning score (EGFR-DLS) was significantly and positively associated with longer progression free survival (PFS) in patients treated with EGFR-TKIs, while EGFR-DLS is significantly and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS among patients treated with ICIs. Thus, the EGFR-DLS provides a non-invasive method for precise quantification of EGFR mutation status in NSCLC patients, which is promising to identify NSCLC patients sensitive to EGFR-TKI or ICI-treatments.

PURPOSE To identify potential gaps in attitudes, knowledge, and institutional practices toward lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ) patients, a national survey of oncologists at National Cancer Institute–Designated Comprehensive Cancer Centers was conducted to measure these attributes related to LGBTQ patients and desire for future training and education. METHODS A random sample of 450 oncologists from 45 cancer centers was selected from the American Medical Association’s Physician Masterfile to complete a survey measuring attitudes and knowledge about LGBTQ health and institutional practices. Results were quantified using descriptive and stratified analyses and by a novel attitude summary measure. RESULTS Of the 149 respondents, there was high agreement (65.8%) regarding the importance of knowing the gender identity of patients, which was contrasted by low agreement (39.6%) regarding the importance of knowing sexual orientation. There was high interest in receiving education regarding the unique health needs of LGBTQ patients (70.4%), and knowledge questions yielded high percentages of “neutral” and “do not know or prefer not to answer” responses. After completing the survey, there was a significant decrease ( P < .001) in confidence in knowledge of health needs for LGB (53.1% agreed they were confident during survey assessment v 38.9% postsurvey) and transgender patients (36.9% v 19.5% postsurvey). Stratified analyses revealed some but limited influence on attitudes and knowledge by having LGBTQ friends and/or family members, political affiliation, oncology specialty, years since graduation, and respondents’ region of the country. CONCLUSION This was the first nationwide study, to our knowledge, of oncologists assessing attitudes, knowledge, and institutional practices of LGBTQ patients with cancer. Overall, there was limited knowledge about LGBTQ health and cancer needs but a high interest in receiving education regarding this community.

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