MRI radiomic features are independently associated with overall survival in soft tissue sarcoma

Purpose: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. Methods and Materials: This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell\u27s concordance index. Results: In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; Conclusions: This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS

Finite element model set-up of colorectal tissue for analyzing surgical scenarios

Finite Element Analysis (FEA) has gained an extensive application in the medical field, such as soft tissues simulations. In particular, colorectal simulations can be used to understand the interaction with the surrounding tissues, or with instruments used in surgical procedures. Although several works have been introduced considering small displacements, as a result of the forces exerted on adjacent tissues, FEA applied to colorectal surgical scenarios is still a challenge. Therefore, this work aims to provide a sensitivity analysis on three geometric models, taking in mind different bioengineering tasks. In this way, a set of simulations has been performed using three mechanical models named Linear Elastic, Hyper-Elastic with a Mooney-Rivlin material model, and Hyper-Elastic with a YEOH material model

PI3K/AKT/mTOR inhibition in combination with doxorubicin is an effective therapy for leiomyosarcoma.

BackgroundLeiomyosarcoma (LMS) is a common type of soft tissue sarcoma that responds poorly to standard chemotherapy. Thus the goal of this study was to identify novel selective therapies that may be effective in leiomyosarcoma by screening cell lines with a small molecule library comprised of 480 kinase inhibitors to functionally determine which signalling pathways may be critical for LMS growth.MethodsLMS cell lines were screened with the OICR kinase library and a cell viability assay was used to identify potentially effective compounds. The top 10 % of hits underwent secondary validation to determine their EC50 and immunoblots were performed to confirm selective drug action. The efficacy of combination drug therapy with doxorubicin (Dox) in vitro was analyzed using the Calcusyn program after treatment with one of three dosing schedules: concurrent treatment, initial treatment with a selective compound followed by Dox, or initial treatment with Dox followed by the selective compound. Single and combination drug therapy were then validated in vivo using LMS xenografts.ResultsCompounds that targeted PI3K/AKT/mTOR pathways (52 %) were most effective. EC50s were determined to validate these initial hits, and of the 11 confirmed hits, 10 targeted PI3K and/or mTOR pathways with EC50 values <1 μM. We therefore examined if BEZ235 and BKM120, two selective compounds in these pathways, would inhibit leiomyosarcoma growth in vitro. Immunoblots confirmed on-target effects of these compounds in the PI3K and/or mTOR pathways. We next investigated if there was synergy with these agents and first line chemotherapy doxorubicin (Dox), which would allow for earlier introduction into patient care. Only combined treatment of BEZ235 and Dox was synergistic in vitro. To validate these findings in pre-clinical models, leiomyosarcoma xenografts were treated with single agent and combination therapy. BEZ235 treated xenografts (n = 8) demonstrated a decrease in tumor volume of 42 % whereas combining BEZ235 with Dox (n = 8) decreased tumor volume 68 % compared to vehicle alone.ConclusionsIn summary, this study supports further investigation into the use of PI3K and mTOR inhibitors alone and in combination with standard treatment in leiomyosarcoma patients

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201