Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features

The aim of this study was to evaluate the variation of radiomics features, defined as “delta radiomics”, in patients undergoing neoadjuvant radiochemotherapy (RCT) for rectal cancer treated with hybrid magnetic resonance (MR)-guided radiotherapy (MRgRT). The delta radiomics features were then correlated with clinical complete response (cCR) outcome, to investigate their predictive power. A total of 16 patients were enrolled, and 5 patients (31%) showed cCR at restaging examinations. T 2*/ T 1 MR images acquired with a hybrid 0.35 T MRgRT unit were considered for this analysis. An imaging acquisition protocol of 6 MR scans per patient was performed: the first MR was acquired at first simulation ( t 0) and the remaining ones at fractions 5, 10, 15, 20 and 25. Radiomics features were extracted from the gross tumour volume (GTV), and each feature was correlated with the corresponding delivered dose. The variations of each feature during treatment were quantified, and the ratio between the values calculated at different dose levels and the one extracted at t 0 was calculated too. The Wilcoxon–Mann–Whitney test was performed to identify the features whose variation can be predictive of cCR, assessed with a MR acquired 6 weeks after RCT and digital examination. The most predictive feature ratios in cCR prediction were the L_least and glnu ones, calculated at the second week of treatment (22 Gy) with a p value = 0.001. Delta radiomics approach showed promising results and the quantitative analysis of images throughout MRgRT treatment can successfully predict cCR offering an innovative personalized medicine approach to rectal cancer treatment.

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“…The contoured images were then exported to Moddicom, a R library developed in our institution to perform radiomics analysis [ 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach

et al. 2018

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“…• Image Feature Data Analysis. This module allow to extract image features for Radiomics (Dinapoli et al, 2015a) analysis from the previously loaded DICOM studies.…”

Section: Methodsmentioning

confidence: 99%

“…In order to provide a common tool for building and finely tuning TCP/NTCP models, in this paper we propose a software library, integrated in a more complete project called moddicom (Dinapoli et al, 2015b), that is able to effectively support the process of generating and improving TCP/NTCP models for the specific data of a radiotherapy centre. To foster the exploitation of the tool, and therefore allow a better use of the knowledge stored in the existing datasets, the proposed software is freely available as an R package at https://github.com/kbolab/moddicom.git.…”

Section: Introductionmentioning

confidence: 99%

RadioBio data: A Moddicom Module to Predict Tumor Control Probability and Normal Tissue Complication Probability in Radiotherapy

Dinapoli

Alitto

Vallati

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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In this work a system for analysing radiotherapy treatment planning dose-volume data is proposed. The work starts from the definition of a framework inside a statistical scripting environment (R) used for creating a software package. The analysis of dose-volume data in radiotherapy of malignant tumours is mandatory for evaluating the prescribed treatment and for feedback analysis of outcome, both in the direction of tumour control and in detection of parameters for estimating and predicting toxicity outcome. The statistical analysis of large amount of clinical data can be slowed by the lack of practice in statistical tools needed, by clinicians, to perform such kind of analysis. This is the reason that lead our working group in the creation of such a tool. Finally an example of clinical application of our software is given for the analysis of the outcome of patients undergoing to radiotherapy for prostate cancer.

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“…The growing interest in radiomics lead to the development of several specifically-designed tools; examples include cGITA [9], TexRAD [10,11], moddicom [12], Pyradiomics [13], and CERR [14]. In parallel with the grows of radiomics tools, initiative such as the Image Biomarker Standardisation Initiative [15] and the Radiomics Ontology 1 become important to standardise the different aspects of image processing and features extraction.…”

Section: Introductionmentioning

confidence: 99%

“…First, we introduce an approach -under the form of a medical agent-based decision support system-for supporting the whole radiomics process. In its current implementation, the agent incorporates some of the ideas and functionalities of moddicom [12]. Given a set of medical images, the proposed system is able to extract a wide range of features, to analyse and select them with regards to the outcome to predict, and to generate an optimised predictive model.…”

Section: Introductionmentioning

confidence: 99%

Towards a modular decision support system for radiomics: A case study on rectal cancer

Gatta

Vallati

Dinapoli

et al. 2019

Artificial Intelligence in Medicine

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Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics. A number of tools are available for supporting some of the steps of the radiomics process, but there is a lack of approaches which are able to deal with all the steps of the process. In this paper, we introduce a medical agent-based decision support system capable of handling the whole radiomics process. The proposed system is tested on two independent data sets of patients treated for rectal cancer. Experimental results indicate that the system is able to generate highly performant centre-specific predictive model, and show the issues related to differences in data sets collected by different centres, and how such issues can

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Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features

Cited by 53 publications

References 9 publications

Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach

Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach

RadioBio data: A Moddicom Module to Predict Tumor Control Probability and Normal Tissue Complication Probability in Radiotherapy

Towards a modular decision support system for radiomics: A case study on rectal cancer

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