Three-Dimensional Deep Noninvasive Radiomics for the Prediction of Disease Control in Patients With Metastatic Urothelial Carcinoma treated With Immunotherapy.

INTRODUCTION: Immunotherapy is effective in a small percentage of patients with cancer and no reliable predictive biomarkers are currently available. Artificial Intelligence algorithms may automatically quantify radiologic characteristics associated with disease response to medical treatments. METHODS: We investigated an innovative approach based on a 3-dimensional (3D) deep radiomics pipeline to classify visual features of chest-abdomen computed tomography (CT) scans with the aim of distinguishing disease control from progressive disease to immune checkpoint inhibitors (ICIs). Forty-two consecutive patients with metastatic urothelial cancer had progressed on first-line platinum-based chemotherapy and had baseline CT scans at immunotherapy initiation. The 3D-pipeline included self-learned visual features and a deep self-attention mechanism. According to the outcome to the ICIs, a 3D deep classifier semiautomatically categorized the most discriminative region of interest on the CT scans. RESULTS: With a median follow-up of 13.3 months (95% CI, 11.1-15.6), the median overall survival was 8.5 months (95% CI, 3.1-13.8). According to disease response to immunotherapy, the median overall survival was 3.6 months (95% CI, 2.0-5.2) for patients with progressive disease; it was not yet reached for those with disease control. The predictive accuracy of the 3D-pipeline was 82.5% (sensitivity 96%; specificity, 60%). The addition of baseline clinical factors increased the accuracy to 92.5% by improving specificity to 87%; the accuracy of other architectures ranged from 72.5% to 90%. CONCLUSION: Artificial Intelligence by 3D deep radiomics is a potential noninvasive biomarker for the prediction of disease control to ICIs in metastatic urothelial cancer and deserves validation in larger series.

Adherence to abiraterone or enzalutamide in elderly metastatic castration-resistant prostate cancer.

PURPOSE: To evaluate adherence to abiraterone or enzalutamide for the treatment of metastatic castration-resistant prostate cancer (mCRPC). METHODS: In an observational prospective cohort study, we monitored patients with mCRPC for their adherence to abiraterone or enzalutamide in the pre- or post-chemotherapy setting. RESULTS: Fifty-eight patients with median age of 76 years (range 56-94), age-adjusted Charlson comorbidity score of 10 (range, 4-15), and geriatric G8 score of 14 (range, 6-17) were enrolled. Twenty-two (38%) patients were treated with abiraterone and 36 (62%) with enzalutamide, while forty-two (72%) were in the pre-chemotherapy setting. Forty-seven patients (81%) had a caregiver. Based on the pill counting, a non-adherence rate of 4.8% and 6.2% was observed for the whole period and the first 3 months, respectively, without a statistically significant difference between abiraterone and enzalutamide cohorts. A lower non-adherence rate (1.3%) was reported by patients during the whole period, mainly due to a misperception (77%) and forgetfulness (19%). Non-adherence rate to the fulfilling of the clinical diary was 38% for the whole period. Non-adherence in the whole period was related to the radiological response (p = 0.03) and geriatric G8 score (p = 0.005). By the receiver operating characteristic (ROC) curve based on the radiological response, non-adherence cut-off was 1.87% (p = 0.04). By this non-adherence cut-off, the G8 cut-off was 14.75 (p = 0.0003). CONCLUSION: Non-adherence to abiraterone or enzalutamide for mCRPC may have an impact on disease response and be related to patients' frailty, suggesting their geriatric assessment and clinical interventions to monitor and increase their adherence.

3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma.

Immunotherapy is regarded as one of the most significant breakthroughs in cancer treatment. Unfortunately, only a small percentage of patients respond properly to the treatment. Moreover, to date, there are no efficient bio-markers able to early discriminate the patients eligible for this treatment. In order to help overcome these limitations, an innovative non-invasive deep pipeline, integrating Computed Tomography (CT) imaging, is investigated for the prediction of a response to immunotherapy treatment. We report preliminary results collected as part of a case study in which we validated the implemented method on a clinical dataset of patients affected by Metastatic Urothelial Carcinoma. The proposed pipeline aims to discriminate patients with high chances of response from those with disease progression. Specifically, the authors propose ad-hoc 3D Deep Networks integrating Self-Attention mechanisms in order to estimate the immunotherapy treatment response from CT-scan images and such hemato-chemical data of the patients. The performance evaluation (average accuracy close to 92%) confirms the effectiveness of the proposed approach as an immunotherapy treatment response biomarker.

The Promise of Digital Biopsy for the Prediction of Tumor Molecular Features and Clinical Outcomes Associated With Immunotherapy.

Immunotherapy by immune checkpoint inhibitors has emerged as an effective treatment for a slight proportion of patients with aggressive tumors. Currently, some molecular determinants, such as the expression of the programmed cell death ligand-1 (PD-L1) or the tumor mutational burden (TMB) have been used in the clinical practice as predictive biomarkers, although they fail in consistency, applicability, or reliability to precisely identify the responding patients mainly because of their spatial intratumoral heterogeneity. Therefore, new biomarkers for early prediction of patient response to immunotherapy, that could integrate several approaches, are eagerly sought. Novel methods of quantitative image analysis (such as radiomics or pathomics) might offer a comprehensive approach providing spatial and temporal information from macroscopic imaging features potentially predictive of underlying molecular drivers, tumor-immune microenvironment, tumor-related prognosis, and clinical outcome (in terms of response or toxicity) following immunotherapy. Preliminary results from radiomics and pathomics analysis have demonstrated their ability to correlate image features with PD-L1 tumor expression, high CD3 cell infiltration or CD8 cell expression, or to produce an image signature concordant with gene expression. Furthermore, the predictive power of radiomics and pathomics can be improved by combining information from other modalities, such as blood values or molecular features, leading to increase the accuracy of these models. Thus, "digital biopsy," which could be defined by non-invasive and non-consuming digital techniques provided by radiomics and pathomics, may have the potential to allow for personalized approach for cancer patients treated with immunotherapy.

[Vinflunine: still an option for patients with advanced urothelial carcinoma following immune-checkpoint inhibitors?] / Vinflunina: ancora un'opzione per i pazienti con carcinoma uroteliale avanzato trattati con inibitori dei checkpoint immunitari?

INTRODUCTION: The treatment of metastatic urothelial cancer (mUC) following first-line standard platinum-based chemotherapy and immune checkpoint inhibitors (ICIs) is not yet established. MATERIAL AND METHODS: We investigated the activity and toxicity of vinflunine at the dose, due to previous treatments, of 280 mg i.v. every 21 days until disease progression or limiting toxicity, with instrumental disease reassessment every 3 cycles, in 6 patients aged ≥18 years, with metastatic urothelial carcinoma of the upper or lower urinary tract, with performance status (PS) according to the Eastern Cooperative Oncology Group (ECOG) of 0-2, adequate hematologic function and progressive disease (PD) following first-line platinum-based chemotherapy and second-line ICI. RESULTS: The median age of the 6 patients was 67.5 years (range 63-77) and median PS 1 (range, 0-2). Four patients (67%) had a disease partial response (PR). With a median follow-up of 4.5 months (range, 3-9), 3 patients are alive (50%). The median progression-free survival following vinflunine (PFS-3) was 4 months (range, 1-8), as compared to the PFS-2 (following ICI) of 4 months (range, 2-9) and the PFS-1 (after platinum-based chemotherapy) of 6 months (range, 2-13). The PRs were not associated with the length of PFS-2 of PFS-1, the histologic subtype, primary and metastatic site of the tumour. No grade 3-4 toxicity has been observed; grade 2 asthenia occurred in 3 patients (50%), grade 1 nausea and constipation were observed in one patient (17%), respectively. CONCLUSION: Despite the low number of patients treated, the activity of vinflunine was substantial and suggests its role as chemotherapy line following previous chemotherapy and immunotherapy, deserving further retrospective or prospective investigations in this setting.

Oral Metronomic Vinorelbine in Advanced Non-small Cell Lung Cancer Patients Unfit for Chemotherapy.

AIM: To explore the feasibility and activity of oral metronomic vinorelbine patients with advanced NSCLC not eligible to standard chemotherapy because of old age (≥70 years), and/or poor Eastern Cooperative Oncology Group performance status (≥2), and/or extensive brain or bone disease, and/or active comorbidities (≥2) requiring for pharmacological treatment. PATIENTS AND METHODS: In a prospective phase II not randomized study, patients with stage IV NSCLC unfit to chemotherapy were treated with oral metronomic vinorelbine at 30 mg fixed dose three times a week until disease progression. RESULTS: Fifty patients were treated, 19 (38%) in the first-line setting. Five patients (11%) experienced a grade 3 toxicity; no grade 4 toxicity occurred. Overall disease control rate was 32%, 44% and 26% in first and subsequent lines, respectively (p=0.39). Median OS and PFS were 7.3 months (95% confidence interval [CI]=4.7-10.0) and 2.7 months (95%CI=2.0-3.4), respectively. CONCLUSION: These data support the activity and safety of metronomic vinorelbine in a relevant proportion of patients usually excluded from any specific treatment.