Prostate cancer (PCa) risk categorization based on clinical/PSA testing results in a substantial number of men being overdiagnosed with indolent, early-stage PCa. Clinically non-significant PCa is characterized as the presence of ISUP grade one, where PCa is found in no more than two prostate biopsy cores.MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET have been proposed as tools to predict ISUP grade one patients and consequently reduce overdiagnosis. In this study, Radiomics analysis is applied to MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET maps to quantify tumor characteristics and predict histology-proven ISUP grades. ICC was applied with a threshold of 0.6 to assess the features’ stability with variations in contouring. Logistic regression predictive models based on imaging features were trained on 31 lesions to differentiate ISUP grade one patients from ISUP two+ patients. The best model based on [⁶⁸Ga]Ga-PSMA-11 PET returned a prediction efficiency of 95% in the training phase and 100% in the test phase whereas the best model based on MRI-ADC had an efficiency of 100% in both phases. Employing both imaging modalities, prediction efficiency was 100% in the training phase and 93% in the test phase. Although our patient cohort was small, it was possible to assess that both imaging modalities add information to the prediction models and show promising results for further investigations.

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWI_b2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWI_b2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWI_b2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWI_b2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWI_b2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

The aim of the study was to evaluate the safety and efficacy of a new chemo-radiotherapy regimen for patients with locally advanced pancreatic cancer (LAPC). Patients were treated as follows: gemcitabine 1000 mg/m² on day 1, and oxaliplatin 100 mg/m² on day 2, every two weeks (GEMOX regimen) for 4 cycles, 15 days off, hypofractionated radiotherapy (35 Gy in 7 fractions in 9 consecutive days), 15 days off, 4 additional cycles of GEMOX, restaging. From April 2011 to August 2016, a total of 42 patients with non resectable LAPC were enrolled. Median age was 67 years (range 41–75). Radiotherapy was well tolerated and the most frequently encountered adverse events were mild to moderate nausea and vomiting, abdominal pain and fatigue. In total, 9 patients underwent surgical laparotomy (5 radical pancreatic resection 1 thermoablation and 3 explorative laparotomy), 1 patient became operable but refused surgery. The overall resectability rate was 25%, while the R0 resection rate was 12.5%. At a median follow-up of 50 months, the median progression-free survival and overall survival were 9.3 (95% CI 6.2–14.9) and 15.8 (95% CI 8.2–23.4) months, respectively. The results demonstrate the feasibility of a new chemo-radiotherapy regimen as a potential treatment for unresectable LAPC.

Introduction: Whole body magnetic resonance imaging (WB-MRI) is a promising emerging imaging technology for detecting bone and soft tissue pathology, especially in the onco-hematological field. This study aims to evaluate cancer patients' experience of WB-MRI performed on a 3T scanner compared to other diagnostic total body examinations. Material and method: In this prospective committee-approved study, patients completed a questionnaire in person (n = 134) after undergoing a WB-MRI scan to collect data on their physical and psychological reactions during the scan, the global satisfaction level, and preference for other types of MRI or computed tomography (CT), or positron emission tomography (PET/CT). Of all patients who had performed a CT or PET/CT the previous year, 61.9% had already undergone an MRI. The most common symptoms reported were: 38.1% perceived a localized increase in temperature and 34.4% numbness and tingling of the limbs. The scan time averaged 45 min and was well tolerated by most patients (112, 85.5%). Overall, WB-MRI was appreciated by the majority (121/134—90.3%) of patients who said they would probably undergo the procedure again. Patients preferred the WB-MRI in 68.7% of cases (92/134), followed by CT in 15.7% of cases (21/134) and by PET/CT in 7.4% (10/134), with 8.4% (11/134) of patients without any preference. The preference for imaging modalities was age-dependent (p = 0.011), while (p > 0.05) was independent of sex and a primary cancer site. Conclusion: These results demonstrate a high degree of WB-MRI acceptance from a patient's point of view.

The authors wish to make the following corrections to this paper