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The emergence of immune checkpoint inhibitors (ICIs) has significantly prolonged the survival time of cancer patients. However, it may also lead to various immune-related adverse events (irAEs), including Guillain–Barré syndrome (GBS), a rare type of irAE. Most GBS patients can recover spontaneously due to the self-limited nature of the disease, but severe cases can result in respiratory failure or even death. Here we report a rare case of GBS occurring in a 58-year-old male patient with non-small cell lung cancer (NSCLC) who developed muscle weakness and numbness of the extremities during chemotherapy combined with KN046, a PD-L1/CTLA-4 bispecific antibody. Despite receiving methylprednisolone and γ-globulin, the patient’s symptoms did not improve. However, there was significant improvement after treatment with mycophenolate mofetil (MM) capsules, which is not a routine regimen for GBS. To the best of our knowledge, this is the first reported case of ICIs-induced GBS that responded well to mycophenolate mofetil instead of methylprednisolone or γ-globulin. Thus, it provides a new treatment option for patients with ICIs-induced GBS.

IntroductionThe treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects. We are currently unable to accurately predict therapeutic response. We aimed to develop a radiomics-based nomogram to predict a major pathological response (MPR) of potentially resectable NSCLC to neoadjuvant immunochemotherapy using pretreatment computed tomography (CT) images and clinical characteristics.MethodsA total of 89 eligible participants were included and randomly divided into training (N=64) and validation (N=25) sets. Radiomic features were extracted from tumor volumes of interest in pretreatment CT images. Following data dimension reduction, feature selection, and radiomic signature building, a radiomics-clinical combined nomogram was developed using logistic regression analysis.ResultsThe radiomics-clinical combined model achieved excellent discriminative performance, with AUCs of 0.84 (95% CI, 0.74-0.93) and 0.81(95% CI, 0.63-0.98) and accuracies of 80% and 80% in the training and validation sets, respectively. Decision curves analysis (DCA) indicated that the radiomics-clinical combined nomogram was clinically valuable.DiscussionThe constructed nomogram was able to predict MPR to neoadjuvant immunochemotherapy with a high degree of accuracy and robustness, suggesting that it is a convenient tool for assisting with the individualized management of patients with potentially resectable NSCLC.