Immune checkpoint inhibitor-related chronic pneumonitis: a case report and literature review

Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography

PURPOSE

Primary barriers to application of immune checkpoint inhibitor (ICI) therapy for cancer include severe side effects (such as potentially life threatening pneumonitis [PN]), which can cause the discontinuation of treatment. Predicting which patients may develop PN while on ICI would improve both safety and potential efficacy because treatments could be safely administered for longer or discontinued before severe toxicity.

METHODS

Starting from a cohort of 3,351 patients with cancer who received previous ICI therapy at the Vanderbilt University Medical Center, we curated 2,700 contrast chest computed tomography (CT) volumes for 671 patients. Three different pure imaging models predicted the potential for PN using only a single time point before the first ICI dose.

RESULTS

The first model used 109 radiomics features only and achieved an AUC of 0.747 (CI, 0.705 to 0.789) with a positive predictive value (PPV) of 0.244 (CI, 0.211 to 0.276) at a sensitivity of 0.553 (CI, 0.485 to 0.621) using mainly features describing the global lung properties. The second model used a convolutional neural network (CNN) on the raw CTs to improve to an AUC of 0.819 (CI, 0.781 to 0.857) with a PPV of 0.244 (CI, 0.203 to 0.284) at a sensitivity of 0.743 (CI, 0.681 to 0.806). The third model combined both radiomics and deep learning but, with an AUC of 0.829 (CI, 0.797 to 0.862) and a PPV of 0.254 (CI, 0.228 to 0.281) at a sensitivity of 0.780 (CI, 0.721 to 0.840), did not show a significant improvement on the CNN-only model.

CONCLUSION

This new model suggests the utility of deep learning in PN prediction over traditional pure radiomics and promises better management for patients receiving ICI and the ability to better stratify patients in immunotherapy drug trials.

Fever of unknown origin associated with immune checkpoint inhibitors

Since the approval for the treatment of melanoma in 2014, immune checkpoint inhibitors (ICIs) have revolutionized the therapy pattern across various malignancies. Coinciding with their frequent usage, their adverse effects, including fever, cannot be neglected. In the context of cancer diseases and cancer treatments, fever of unknown origin (FUO), which has long posed a challenge for clinicians in terms of diagnosis and management, brings forth new connotation and significance. In this paper review, we present the concept of ICIs-associated FUO, consider activated immune system and elevated cytokines as common mechanisms by which ICIs induce fever and various immune-related adverse events (irAEs), summarize and compare the primary etiologies of ICI-associated FUO, and compare it with conventional types of FUO.