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Background: Altered body composition has been attributed to major health problems globally, particularly in patients with cancer. To date, there have not been sufficient indices for body compositions in predicting the survival of locally advanced breast cancer (LABC). Purpose: To assess the association between body composition and overall survival (OS) as well as disease-free survival (DFS) in patients with LABC. Material and Methods: A retrospective study was conducted of patients with LABC diagnosed between 2010 and 2018. Body composition parameters, including skeletal muscle and adiposity parameters, were measured using computed tomography at the L3 vertebra using in-house software developed by MATLAB and freeware Python 3.6.13. The association between body composition and OS and DFS were analyzed using a log-rank test and multivariate Cox-proportional hazard regression. Results: Of 199 patients, 72 (36%) died during the follow-up period (range = 3.8–150.7 months). Median survival was 101 months. Low visceral-to-subcutaneous ratio ≤0.3 (adjusted hazard ratio [aHR] = 2.57, 95% confidence interval [CI] = 1.51−4.37; aHR = 2.46, 95% CI = 1.33−4.56), and high composite fat (aHR = 3.26, 95% CI = 1.69−6.29; aHR = 2.19, 95% CI = 1.11−4.3) were associated with lower OS and DFS. Positive lymph nodes ≥3, progesterone receptor negative, and total radiation dose >5000 cGy significantly decreased both OS and DFS. A history of previous treatment before body composition assessment and surgery had a protective effect on OS and DFS. No association of sarcopenia, body mass index, and adiposity areas with survival outcomes was observed. Conclusion: Low visceral-to-subcutaneous ratio and high composite fat were independent prognostic factors for OS and DFS in patients with LABC. However, other body composition parameters showed no effect on survival. [ABSTRACT FROM AUTHOR]

Background: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features. Purpose: To evaluate the prognostic value of preoperative computed tomography (CT) image texture features and deep learning self-learning high-throughput features (SHF) on postoperative overall survival in the treatment of patients with colorectal cancer (CRC). Material and Methods: The dataset consisted of 810 enrolled patients with CRC confirmed from 10 November 2011 to 10 February 2018. In contrast, SHF extracted by deep learning with multi-task training mechanism and texture features were extracted from the CT with tumor volume region of interest, respectively, and combined with the Cox proportional hazard (CoxPH) model for initial validation to obtain a RAD score to classify patients into high- and low-risk groups. The SHF stability was further validated in combination with Neural Multi-Task Logistic Regression (N-MTLR) model. The overall recognition ability and accuracy of CoxPH and N-MTLR model were evaluated by C-index and Integrated Brier Score (IBS). Results: SHF had a more significant degree of differentiation than texture features. The result is (SHF vs. texture features: C-index: 0.884 vs. 0.611; IBS: 0.025 vs. 0.073) in the CoxPH model, and (SHF vs. texture features: C-index: 0.861 vs. 0.630; IBS: 0.024 vs. 0.065) in N-MTLR. Conclusion: SHF is superior to texture features and has potential application for the preoperative prediction of the individualized treatment of CRC. [ABSTRACT FROM AUTHOR]

Background: Interstitial brachytherapy (iBT) has become a viable treatment option in the therapy of early and intermediate stage hepatocellular carcinoma (HCC). Prognostic imaging tools to predict patient outcome are missing. Purpose: To assess the predictive value of baseline diffusion-weighted imaging in HCC before iBT with regard to local tumor control and overall survival (OS). Material and Methods: We retrospectively identified 107 patients who underwent iBT for HCC from 2011 to 2018 from our database. Apparent diffusion coefficient (ADC) values for each treated lesion were analyzed in region of interest measurements. Additionally, explorative combined ratios adjusting total measured lesion area and mean measured lesion area per patient by ADC values were calculated. Measurements underwent a univariate and multivariate Cox regression analysis. The log rank test was then used to verify prognostic cutoff levels for median survival time. Results: A total of 189 lesions in 81 patients were measured. Median survival of patients was 46.0 months. Neither ADC parameter was indicative of local tumor control. Lesion size >5 cm was associated with lower local tumor control (hazard ratio [HR]=4.292, 95% confidence interval [CI]=1.285–14.331; P = 0.018). Average measured lesion area divided by ADCmin (ADCarea mean, min) was identified to independently predict OS (HR=1.994, 95% CI=1.172–3.392; P = 0.011). A cutoff based on the variable's median (0.29 × 10–4 AU) identified patients with poor outcome (OS 36 vs. 61 months) for lower ADCarea mean, min values as verified by the log-rank test (P = 0.040). Conclusion: Pre-treatment ADCarea mean, min may serve as an independent predictor of OS in patients with HCC undergoing iBT. [ABSTRACT FROM AUTHOR]

Background: Complete resection is the only potentially curative treatment in patients with pancreatic ductal adenocarcinoma (PDA) and is associated with a longer overall survival (OS) than incomplete resection of tumor. Hence, prediction of the resection status after surgery would help predict the prognosis of patients with PDA. Purpose: To predict residual tumor (R) classification and OS in patients who underwent first-line surgery for PDA using preoperative magnetic resonance imaging (MRI). Material and Methods: In this study, 210 patients with PDA who underwent MRI and first-line surgery were randomly categorized into a test group (n=150) and a validation group (n=60). The R classification was divided into R0 (no residual tumor) and R1/R2 (microscopic/macroscopic residual tumor). Preoperative MRI findings associated with R classification and OS were assessed by using logistic regression and Cox proportional hazard models. In addition, the prediction models for the R classification and OS were validated using calibration plots and C statistics. Results: On preoperative MRI, portal vein encasement (odds ratio 4.755) was an independent predictor for R1/R2 resection (P =0.040). Tumor size measured on MRI (hazard ratio [HR] per centimeter 1.539) was a predictor of OS, along with pathologic N1 and N2 stage (HR 1.944 and 3.243, respectively), R1/R2 resection (HR 3.273), and adjuvant chemoradiation therapy (HR 0.250) (P <0.050). Calibration plots demonstrated satisfactory predictive performance. Conclusion: Preoperative MRI was valuable for predicting R1/R2 resection using portal vein encasement. Tumor size measured on MRI was useful for the prediction of OS after first-line surgery for PDA. [ABSTRACT FROM AUTHOR]

Background: To overcome the technical difficulty of bilateral stent-in-stent placement, large cell-type biliary stents have been developed. However, most of the studies using large cell-type stents were conducted with endoscopic method. Purpose: To evaluate the efficacy and safety of percutaneous stent placement with a stent-in-stent method using large cell-type stents in patients with malignant hilar biliary obstruction. Material and Methods: From December 2015 and October 2018, 51 patients with malignant hilar biliary obstruction were retrospectively studied. All of the patients underwent bilateral (n=46) or unilateral (n=5) stenting in a T, Y, or X configuration with a stent-in-stent method using large cell-type stents. Technical success, complications, successful internal drainage, stent patency, and patient survival were analyzed. Results: A total of 118 stents were successfully placed in 51 patients (100.0%). Three patients had minor complications with self-limiting hemobilia. Major complications were not observed in any patient. Successful internal drainage was achieved in 45 patients (88.2%). Clinical follow-up information until death or the end of the study was available for 50 of 51 patients. The median patient survival was 285.5 days (95% confidence interval [CI] 197–374). Stent dysfunction occurred in 16 patients (35.6%) due to tumor ingrowth (n=9) or tumor ingrowth combined with biliary sludge (n=7) among the patients who achieved successful internal drainage. Median stent patency was 179 days (95% CI 104–271). Conclusion: Percutaneous stent-in-stent placement with large cell-type stents is technically feasible and safe, and can be an effective technique in patients with malignant hilar biliary obstruction. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma multiforme (GBM) is the commonest malignant primary brain tumor and still has one of the worst prognoses among cancers in general. There is a need for non-invasive methods to predict individual prognosis in patients with GBM. Purpose: To evaluate quantitative volumetric tissue assessment of enhancing tumor volume on cranial magnetic resonance imaging (MRI) as an imaging biomarker for predicting overall survival (OS) in patients with GBM. Material and Methods: MRI scans of 49 patients with histopathologically confirmed GBM were analyzed retrospectively. Baseline contrast-enhanced (CE) MRI sequences were transferred to a segmentation-based three-dimensional quantification tool, and the enhancing tumor component was analyzed. Based on a cut-off percentage of the enhancing tumor volume (PoETV) of >84.78%, samples were dichotomized, and the OS and intracranial progression-free survival (PFS) were evaluated. Univariable and multivariable analyses, including variables such as sex, Karnofsky Performance Status score, O 6-methylguanine-DNA-methyltransferase status, age, and resection status, were performed using the Cox regression model. Results: The median OS and PFS were 16.9 and 7 months in the entire cohort, respectively. Patients with a CE tumor volume of >84.78% showed a significantly shortened OS (12.9 months) compared to those with a CE tumor volume of ≤84.78% (17.7 months) (hazard ratio [HR] 2.72; 95% confidence interval [CI] 1.22–6.03; P = 0.01). Multivariable analysis confirmed that PoETV had a significant prognostic role (HR 2.47; 95% CI 1.08–5.65; P = 0.03). Conclusion: We observed a correlation between PoETV and OS. This imaging biomarker may help predict the OS of patients with GBM. [ABSTRACT FROM AUTHOR]