MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models.

Methods

Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers.

Results

Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher ( p = 0.047) for the automatic versus manual segmentation.

Conclusion

Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-022-00272-2.

Related collections

Most cited references 36

Record: found
Abstract: found
Article: not found

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

0 comments Cited 33310 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah … (2020)

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

0 comments Cited 1050 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Radiomics: the facts and the challenges of image analysis

Stefania Rizzo, Francesca Botta, Sara Raimondi … (2018)

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

0 comments Cited 334 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Arianna Defeudis:

ORCID: http://orcid.org/0000-0002-1745-3044

arianna.defeudis@unito.it

Journal

Journal ID (nlm-ta): Eur Radiol Exp

Journal ID (iso-abbrev): Eur Radiol Exp

Title: European Radiology Experimental

Publisher: Springer Vienna (Vienna )

ISSN (Electronic): 2509-9280

Publication date (Electronic): 3 May 2022

Publication date PMC-release: 3 May 2022

Publication date Collection: December 2022

Volume: 6

Electronic Location Identifier: 19

Affiliations

[1 ]GRID grid.419555.9, ISNI 0000 0004 1759 7675, Department of Radiology, , Candiolo Cancer Institute, FPO-IRCCS, ; Candiolo, Italy

[2 ]GRID grid.7605.4, ISNI 0000 0001 2336 6580, Department of Surgical Sciences, , University of Turin, ; Turin, Italy

[3 ]Radiology Unit, SS Annunziata Savigliano Hospital, Cuneo, Italy

[4 ]GRID grid.4800.c, ISNI 0000 0004 1937 0343, Politecnico di Torino, Electronic and Telecommunication Department (DET), ; Turin, Italy

[5 ]GRID grid.414700.6, ISNI 0000 0004 0484 5983, Mauriziano hospital, ; Turin, Italy

[6 ]GRID grid.7605.4, ISNI 0000 0001 2336 6580, Radiology Unit, Department of Surgical Sciences, , University of Turin, ; Turin, Italy

Author information

Arianna Defeudis http://orcid.org/0000-0002-1745-3044

Article

Publisher ID: 272

DOI: 10.1186/s41747-022-00272-2

PMC ID: 9061921

PubMed ID: 35501512

SO-VID: 91fc98ca-4951-491e-84aa-12b229a415e7

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 14 December 2021

Date accepted : 23 March 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100005010, Associazione Italiana per la Ricerca sul Cancro;

Award ID: ID. 21091 program

Award Recipient : Daniele Regge

Funded by: FundRef http://dx.doi.org/10.13039/501100003197, Agenzia Italiana del Farmaco, Ministero della Salute;

Award ID: FPRC 5xmille 2015 - strategy

Award Recipient : Daniele Regge

Custom metadata

Keywords: artificial intelligence,machine learning,multiparametric magnetic resonance imaging,neoadjuvant therapy,rectal neoplasms

Data availability:

Keywords: artificial intelligence, machine learning, multiparametric magnetic resonance imaging, neoadjuvant therapy, rectal neoplasms

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

Read this article at

Abstract

Background

Methods

Results

Conclusion

Supplementary Information

Related collections

Artificial Intelligence in Medicine

Most cited references 36

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Radiomics: the facts and the challenges of image analysis

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 212

Cited by 12

Most referenced authors 836