A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer

Abstract

Purpose Aim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner. Methods In two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with Z-score normalisation and an initial feature selection was carried out using Wilcoxon-Mann-Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness. Results Three features were selected: maximum fractal dimension with IB = 0-50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0-50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively. Conclusions The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.

Structured Reporting of Rectal Cancer Staging and Restaging: A Consensus Proposal

“…The section “Patient Clinical Data” is designed to go beyond simple patient history collection, containing data regarding the family history of oncological pathologies and the exposure to different risk factors as well as data regarding any genetic mutations. These data could create the basis of a large database, allowing not only for the carrying out of epidemiological statistical analysis (i.e., family history and geographical distribution of cancer), but which could be used to build a Radiomics model by combining radiological features and clinical data [ 23 ]. In this context, the added value of genomic data could be used to develop a model of Radiogenomics, which was helpful regarding the highest level of personalized risk stratification and the advanced precision medicine process [ 24 , 25 ].…”

Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy

“…In absence of an external validation dataset, the robustness of the delta radiomics feature identified was evaluated by means of a five folds cross-validation analysis with tree iterations [ 39 ].…”

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

“…Radiomics consists of the extraction of several parameters by radiological data that can provide information about tumor phenotype as well as the cancer microenvironment [152][153][154][155][156][157][158][159][160]. Radiomics, when combined with other data linked to patient outcome, can produce precise evidence-basedclinical-decision support systems.…”