Significant advances in lymph node involvement (LNI) risk modeling in prostate cancer (PCa) have been achieved with the addition of visual interpretation of magnetic resonance imaging (MRI) data, but it is likely that quantitative analysis could further improve prediction models. In this study, we aimed to develop and internally validate a novel LNI risk prediction model based on radiomic features extracted from preoperative multimodal MRI. All patients who underwent a preoperative MRI and radical prostatectomy with extensive lymph node dissection were retrospectively included in a single institution. Patients were randomly divided into the training (60%) and testing (40%) sets. Radiomic features were extracted from the index tumor volumes, delineated on the apparent diffusion coefficient corrected map and the T2 sequences. A ComBat harmonization method was applied to account for inter-site heterogeneity. A prediction model was trained using a neural network approach (Multilayer Perceptron Network, SPSS v24.0©) combining clinical, radiomic and all features. It was then evaluated on the testing set and compared to the current available models using the Receiver Operative Characteristics and the C-Index. Two hundred and eighty patients were included, with a median age of 65.2 y (45.3–79.6), a mean PSA level of 9.5 ng/mL (1.04–63.0) and 79.6% of ISUP ≥ 2 tumors. LNI occurred in 51 patients (18.2%), with a median number of extracted nodes of 15 (10–19). In the testing set, with their respective cutoffs applied, the Partin, Roach, Yale, MSKCC, Briganti 2012 and 2017 models resulted in a C-Index of 0.71, 0.66, 0.55, 0.67, 0.65 and 0.73, respectively, while our proposed combined model resulted in a C-Index of 0.89 in the testing set. Radiomic features extracted from the preoperative MRI scans and combined with clinical features through a neural network seem to provide added predictive performance compared to state of the art models regarding LNI risk prediction in PCa.

Background: Cancer is the second cause of disease-related deaths worldwide. Malnutrition among cancer patients is very common, with an estimated incidence of approximately 40 to 80%. While it is already a proven fact that malnutrition is prevalent among cancer patients, its impact on the quality of life of patients has not been adequately studied, particularly in the local setting. Purpose: To assess quality of life, nutrition status and to determine the affects of nutrition status on quality of life of cancer patients treated chemotherapy at Oncology Department, Hue University of Medicine and Pharmacy Hospital. Methods: A cross sectional study with 70 cancer patients admitted for chemotherapy recruited from at oncology department, Hue University of Medicine and Pharmacy Hospital during March to September 2018. The EORTC QLQ-C30 were used to assess quality of life and Subjective Global Assessment scale were used to assess nutrition status. T-test, ANOVA, Mann Whitney, Kruskal Wallis were used to determine the correlation between 2 factors. Pearson and Spearman Coefficient were used to measure the strength of relationship between the factors. Results: Patient’s mean age was 58.93 ± 13.26, males were 61.3%. There were 67.14% patients with SGA A, 14.29% were classified SGA-B (moderately malnourished) and 18.57% were classified SGA C (severely malnourished). The global health scale, the functional scales were in the limit of the EORTC reference value, meanwhile the toxicities -related symptom scales were worse than the EORTC reference value. Patients were statistically different across the Subjective Global Assessment groups according to emotional (p < 0.05), and cognitive functioning (p < 0.05) nausea and vomiting (p < 0.05). Conclusions: This study showed that there were the effects of nutrition status on quality of life in patients treated chemotherapy. Key words: Quality of life, nutritional status, cancer, chemotherapy

BACKGROUND Current prostate biopsy (PBx) protocol for prostate cancer (PCa) diagnosis is to perform systematic biopsies (SBx) combined with targeted biopsies (TBx) in case of positive MRI (i.e PI-RADS ≥3). To assess the utility of performing SBx in combination with TBx, we determined the added value of SBx brought to the diagnosis of PCa according to their sextant location and MRI target characteristics. METHODS In our local prospectively collected database, we conducted a single-center retrospective study including all patients with a suspicion of PCa, who underwent transrectal ultrasound guided (TRUS) prostate biopsies (PBx) with a prior MRI and a single lesion classified as PI-RADS ≥3. We have characterized the SBx according to their location on MRI: same sextant (S-SBx), adjacent sextant (A-SBx), ipsilateral side (I-SBx) and contralateral side (C-SBx). The added value of SBx and TBx was defined as any upgrading to significant PCa (csPCa) (ISUP ≥2). RESULTS 428 patients were included in the study. The added value of SBx was 10% overall. Regarding the lesion location and the SBx sextant, the added value of SBx was: 5.1% for S-SBx, 5.6% for A-SBx, 4.9 % for I-SBx and 2.1% for C-SBx. The overall added value of SBx was 6.0% for PI-RADS 3 lesions, 15% for PI-RADS 4 lesions and 6.7% for PI-RADS 5 lesions (p = 0,017). The added value of SBx for contralateral side was 1.7% (2/117), 3.1% (6/191) and 0.8% (1/120) for PI-RADS 3, PI-RADS 4 and PI-RADS 5 lesions, respectively (p = 0,4). The added value of SBx was lower when the number of TBx was higher (OR 0.56; CI95% 0.38-0.82; p = 0.003). CONCLUSIONS Our results suggest that the utility of performing SBx in the contralateral lobe toward the MRI lesion was very low, supporting that they might be avoided.