Background and purpose: Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that enhance the immune response against cancer cells. ICIs are generally well tolerated, although endocrine immune-related adverse events (irAEs) are common. We investigated the risk factors for thyroid irAEs in patients treated with ICIs. Moreover, we evaluated the clinical outcome of subjects who became hypothyroid compared to euthyroid patients.Patients and methods: We retrospectively analyzed a series of 195 consecutively subjects treated with ICIs for metastatic tumors at the University of Naples “Federico II” between January 2014 and March 2020. Only subjects tested for thyroid function before and during the treatment with ICIs were included.Results: In the 96 patients treated with ICIs who were included [66 males, median age: 62 years (27–87)], thyroid irAEs occurred in 36 (37.5%), 16 (16.7%) a transient thyrotoxicosis, and 20 (20.8%) an hypothyroidism (in nine subjects hypothyroidism was preceded by a transient thyrotoxicosis). Only baseline TSH levels above 1.67 mIU/L and positive anti-thyroid antibodies (Ab-T) were associated with a higher risk of hypothyroidism. Patients with hypothyroidism during ICI treatment showed an improved 2-year PFS (HR = 0.82 CI 0.47–1.43;p = 0.0132) and OS (HR = 0.38 CI 95% 0.17–0.80;p = 0.011) compared to euthyroid patients.Conclusions: Baseline TSH levels above 1.67 mIU/L and presence of Ab-T are risk factors for the development of thyroid irAEs. Patients affected by thyroid irAEs showed a longer survival than patients who remained euthyroid.

: Immunotherapy based on anti PD-1/PD-L1 inhibitors has proven to be more effective than sunitinib in the first-line setting of advanced renal cell carcinoma (RCC). RCC patients with sarcomatoid histology (sRCC) have a poor prognosis and limited therapeutic options. We performed a systematic review and a meta-analysis of randomized-controlled trials (RCTs) of first-line anti PD-1/PDL-1 agents vs. sunitinib, presenting efficacy data in the sub-group of sRCC patients. The systematic research was conducted on Google Scholar, Cochrane Library, PubMed and Embase and updated until 31th January, 2020. Abstracts from ESMO and ASCO (2010–2019) were also reviewed. Full texts and abstracts reporting about RCTs testing first-line anti-PD-1/ PD-L1 agents vs. sunitinib in RCC were included if sRCC sub-group analyses of either PFS (progression-free survival), OS (overall survival) or radiological response rate were available. Pooled data from 3814 RCC patients in the ITT (intention-to-treat) population and from 512 sRCC patients were included in the quantitative synthesis. In the sRCC sub-group vs. the ITT population, pooled estimates of the PFS-HRs were 0.57 (95%: 0.45–0.74) vs. 0.79 (95% CI: 0.70–0.89), respectively, with a statistically meaningful interaction favoring the sRCC sub-group (pooled ratio of the PFS-HRs = 0.64; 95% CI: 0.50–0.82; p < 0.001). Pooled estimates of the difference in CR-R (complete response-rate) achieved with anti-PD-1/PDL-1 agents vs. sunitinib were + 0.10 (95% CI: 0.04–0.16) vs. + 0.04 (95% CI: 0.00–0.07) in the sRCC vs. the non-sRCC sub groups, with a statistically meaningful difference of + 0.06 (95% CI: 0.02–0.10; p = 0.007) favoring the sRCC sub-group. Sarcomatoid histology may be associated with improved efficacy of anti PD-1/PDL-1 agents vs. sunitinib in terms of PFS and CR-R.

The aim of our study was to predict the occurrence of distant metastases in non-small-cell lung cancer (NSCLC) patients using machine learning methods and texture analysis of ¹⁸F-labeled 2-deoxy-d-glucose Positron Emission Tomography/Computed Tomography {[¹⁸F]FDG PET/CT} images. In this retrospective and single-center study, we evaluated 79 patients with advanced NSCLC who had undergone [¹⁸F]FDG PET/CT scan at diagnosis before any therapy. Patients were divided into two independent training (n = 44) and final testing (n = 35) cohorts. Texture features of primary tumors and lymph node metastases were extracted from [¹⁸F]FDG PET/CT images using the LIFEx program. Six machine learning methods were applied to the training dataset using the entire panel of features. Dedicated selection methods were used to generate different combinations of five features. The performance of selected machine learning methods applied to the different combinations of features was determined using accuracy, the confusion matrix, receiver operating characteristic (ROC) curves, and area under the curve (AUC). A total of 104 and 78 lesions were analyzed in the training and final testing cohorts, respectively. The support vector machine (SVM) and decision tree methods showed the highest accuracy in the training cohort. Seven combinations of five features were obtained and introduced in the models and subsequently applied to the training and final testing cohorts using the SVM and decision tree. The accuracy and the AUC of the decision tree method were higher than those obtained with the SVM in the final testing cohort. The best combination of features included shape sphericity, gray level run length matrix_run length non-uniformity (GLRLM_RLNU), Total Lesion Glycolysis (TLG), Metabolic Tumor Volume (MTV), and shape compacity. The combination of these features with the decision tree method could predict the occurrence of distant metastases with an accuracy of 74.4% and an AUC of 0.63 in NSCLC patients.

We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from ¹⁸F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent ¹⁸F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTV_TOT) and whole-body Total Lesion Glycolysis (TLG_WB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTV_TOT, TLG_WB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTV_TOT (p = 0.0050), TLG_WB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTV_TOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTV_TOT ≤ 89.5 mL had higher OS than those with MTV_TOT > 89.5 mL (p = 0.0063). Combining CoV and MTV_TOT, patients with CoV ≤ 0.38 and MTV_TOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.

Objectives: The aim of the present study was to test whether a parameter reflecting tumor dissemination (Dmax), derived from basal 18F-FDG PET/CT, may predict clinical outcome in patients with advanced non-small-cell lung cancer (NSCLC). Methods: A total of 78 patients (55 men, 23 women) with stage III and IV NSCLC who had undergone whole-body 18F-FDG PET/CT scan at diagnosis were included in this study. Imaging parameters of primary lung tumors along with total MTV (MTV_TOT) and whole-body TLG (TLG_WB) of all malignant lesions were determined. Moreover, the largest distance between two 18F-FDG avid lesions (Dmax) in each patient was measured. Univariate and multivariate analyses of clinical and imaging variables were performed followed by overall survival (OS) curves. Results: A total of 441 lesions were analyzed, including 78 primary tumors, 174 metastatic lymph nodes, and 189 distant metastases. In primary tumors, the average values of SUVmax, SUVmean, MTV, and TLG were 11.80 ± 5.73, 5.37 ± 2.09, 60.61 ± 102.57 mL, and 340.36 ± 558.40 g, respectively. The mean value of Dmax was 29.98 ± 20.98 cm, whereas the average values of MTV_TOT and TLG_WB were 155.90 ± 176.94 mL and 851.08 ± 1032.17 g, respectively. In the univariate analysis, OS was predicted by MTV_TOT (p = 0.0145), TLG_WB (p = 0.0518), Dmax (p = 0.0031), and stage (p = 0.0130), whereas in the multivariate analysis, only Dmax was retained in the model (χ² = 7.3130, p = 0.0068). In particular, a high Dmax value indicates a worse prognosis. Moreover, the combination of Dmax with MTV_TOT was able to improve the prognostic stratification of patients with advanced stages of NSCLC. Conclusions: Dmax, by reflecting tumor dissemination throughout the body, can predict overall survival in NSCLC patients.