Safety of extended interval dosing immune checkpoint inhibitors: a multicenter cohort study

BACKGROUND: Real-life spectrum and survival implications of immune-related adverse events (irAEs) in patients treated with extended interval dosing (ED) immune checkpoint inhibitors (ICIs) are unknown. METHODS: Characteristics of 812 consecutive solid cancer patients who received at least 1 cycle of ED monotherapy (pembrolizumab 400 mg Q6W or nivolumab 480 mg Q4W) after switching from canonical interval dosing (CD; pembrolizumab 200 mg Q3W or nivolumab 240 mg Q2W) or treated upfront with ED were retrieved. The primary objective was to compare irAEs patterns within the same population (before and after switch to ED). irAEs spectrum in patients treated upfront with ED and association between irAEs and overall survival were also described. RESULTS: A total of 550 (68%) patients started ICIs with CD and switched to ED. During CD, 225 (41%) patients developed any grade and 17 (3%) G3 or G4 irAEs; after switching to ED, any grade and G3 or G4 irAEs were experienced by 155 (36%) and 20 (5%) patients. Switching to ED was associated with a lower probability of any grade irAEs (adjusted odds ratio [aOR] = 0.83, 95% confidence interval [CI] = 0.64 to 0.99; P = .047), whereas no difference for G3 or G4 events was noted (aOR = 1.55, 95% CI = 0.81 to 2.94; P = .18). Among patients who started upfront with ED (n = 232, 32%), 107 (41%) developed any grade and 14 (5%) G3 or G4 irAEs during ED. Patients with irAEs during ED had improved overall survival (adjusted hazard ratio [aHR] = 0.53, 95% CI = 0.34 to 0.82; P = .004 after switching; aHR = 0.57, 95% CI = 0.35 to 0.93; P = .025 upfront). CONCLUSIONS: Switching ICI treatment from CD and ED did not increase the incidence of irAEs and represents a safe option also outside clinical trials

In Vivo evaluation of the Chemical Composition of Urinary Stones Using Dual-Energy CT

OBJECTIVE. The purpose of this article is to evaluate in vivo the chemical composition of urinary stones using dual-source and dual-energy CT, with crystallography as the reference standard. MATERIALS AND METHODS. Forty patients (mean [\ub1 SD] age, 49 \ub1 17 years) with known or suspected nephrolithiasis underwent unenhanced abdominal CT for urinary tract evaluation using a dual-energy technique (tube voltages, 140 and 80 kVp). For each stone 5 mm or larger in diameter, we evaluated the site, diameter, CT density, surface (smooth vs rough), and stone composition. Patients were treated with extracorporeal shock wave lithotripsy (n = 34), percutaneous nephrolithotomy (n = 4), or therapeutic ureterorenoscopy (n = 2). Collected stones underwent crystallography, and the agreement with the results of dual-energy CT was calculated with the Cohen kappa coefficient. The correlation among stone composition, diameter, and CT density was estimated using the Kruskal-Wallis test. RESULTS. Thirty-one patients had a single stone and nine had multiple stones, for a total of 49 stones. Forty-five stones were in the kidneys, and four were in the ureters; 23 had a smooth surface and 26 had a rough surface. The mean stone diameter was 12 \ub1 6 mm; mean CT density was 783 \ub1 274 HU. According to crystallography, stone composition was as follows: 33 were calcium oxalate, seven were cystine, four were uric acid, and five were of mixed composition. Dual-energy CT failed to identify four stones with mixed composition, resulting in substantial agreement between dual-energy CT and crystallography (Cohen \u3ba = 0.684). Stone composition was not correlated with either stone diameter (p = 0.920) or stone CT density (p = 0.185). CONCLUSION. CT showed excellent accuracy in classifying urinary stone chemical composition, except for uric acid\u2013hydroxyapatite mixed stones

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy

Simple Summary: In this paper, the authors show that artificial intelligence (AI) and machine learning (ML) are useful approaches to integrate multifactorial data and helpful for personalized prediction. In detail, compared to PD-L1 for advanced non-small cell lung cancer (NSCLC), ML tools predicted better responder (R) and non-responder (NR) patients to immunotherapy (IO). It was also able to indirectly foresee OS and PFS of R and NR patients. Given the high incidence of NSCLC, and the absence of reliable biomarkers to predict the response to IO other than PD-L1, the authors believe this research may be of great interest to anyone involved in thoracic oncology. Furthermore, given the growing interest from the scientific community in AI and ML, the authors believe that this manuscript could represent a fascinating topic to anyone who needs to exploit the enormous potential of these tools in the treatment of cancer. Abstract: (1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO

Retreatment With Anti-EGFR Antibodies in Metastatic Colorectal Cancer Patients: A Multi-institutional Analysis

Liquid biopsy is a promising tool to predict benefit from anti–epidermal growth factor receptor (EGFR) retreatment in metastatic colorectal cancer patients. However, this technique is far from ready for routine clinical practice. Therefore, we identified 86 patients in a real-life database of 5530 patients to explore clinical features expected to predict benefit from anti-EGFR retreatment; however, none of these factors was predictive of response to anti-EGFR retreatment