Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?

Giraud, Nicolas; Sargos, P.; Leduc, Nicolas; Saut, Olivier; Vuong, T.; Vendrely, V.

doi:10.3390/app10061988

Cited by 3 publications

(4 citation statements)

References 45 publications

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“…In that regard, radiomics features extracted from medical imaging could bring additional informative data. Adding these novel parameters to previously used clinical parameters seems to offer additive performance, paving the way to build more accurate predictive models, but have for now barely been explored in ASCC [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

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MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

Giraud

Saut

Aparicio

et al. 2021

Cancers

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Purpose: Chemo-radiotherapy (CRT) is the standard treatment for non-metastatic anal squamous cell carcinomas (ASCC). Despite excellent results for T1-2 stages, relapses still occur in around 35% of locally advanced tumors. Recent strategies focus on treatment intensification, but could benefit from a better patient selection. Our goal was to assess the prognostic value of pre-therapeutic MRI radiomics on 2-year disease control (DC). Methods: We retrospectively selected patients with non-metastatic ASCC treated at the CHU Bordeaux and in the French FFCD0904 multicentric trial. Radiomic features were extracted from T2-weighted pre-therapeutic MRI delineated sequences. After random division between training and testing sets on a 2:1 ratio, univariate and multivariate analysis were performed on the training cohort to select optimal features. The correlation with 2-year DC was assessed using logistic regression models, with AUC and accuracy as performance gauges, and the prediction of disease-free survival using Cox regression and Kaplan-Meier analysis. Results: A total of 82 patients were randomized in the training (n = 54) and testing sets (n = 28). At 2 years, 24 patients (29%) presented relapse. In the training set, two clinical (tumor size and CRT length) and two radiomic features (FirstOrder_Entropy and GLCM_JointEnergy) were associated with disease control in univariate analysis and included in the model. The clinical model was outperformed by the mixed (clinical and radiomic) model in both the training (AUC 0.758 versus 0.825, accuracy of 75.9% versus 87%) and testing (AUC 0.714 versus 0.898, accuracy of 78.6% versus 85.7%) sets, which led to distinctive high and low risk of disease relapse groups (HR 8.60, p = 0.005). Conclusion: A mixed model with two clinical and two radiomic features was predictive of 2-year disease control after CRT and could contribute to identify high risk patients amenable to treatment intensification with view of personalized medicine.

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Section: Discussionmentioning

confidence: 99%

“…The addition of these radiomic indicators display promising results in the field of oncology for a wide range of diseases [ 18 , 19 ]. In ASCC specifically, few papers have been published, using mostly PET or MRI imaging, and often small cohort sizes [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

Giraud

Saut

Aparicio

et al. 2021

Cancers

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“…These images provide the opportunity to extract quantitative data which can be used as features within a predictive model, a process termed as radiomics [ 11 ]. Although use of radiomics is widely reported for assessment and prediction in many different disease processes, limited data is available on outcome prediction models in ASCC using radiomic features extracted from PET-CT [ 12 ]. Brown et al used an elastic net model, combining least absolute shrinkage and selection operator (LASSO) and ridge regression for selection of radiomic features to predict progression-free survival (PFS) in ASCC patients treated with radiotherapy, mitomycin C and 5-fluorouracil regimens [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma

Frood,

Mercer,

Brown

et al. 2023

Eur Radiol

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Objectives The incidence of anal squamous cell carcinoma (ASCC) is increasing worldwide, with a significant proportion of patients treated with curative intent having recurrence. The ability to accurately predict progression-free survival (PFS) and overall survival (OS) would allow for development of personalised treatment strategies. The aim of the study was to train and external test radiomic/clinical feature derived time-to-event prediction models. Methods Consecutive patients with ASCC treated with curative intent at two large tertiary referral centres with baseline FDG PET-CT were included. Radiomic feature extraction was performed using LIFEx software on the pre-treatment PET-CT. Two distinct predictive models for PFS and OS were trained and tuned at each of the centres, with the best performing models externally tested on the other centres’ patient cohort. Results A total of 187 patients were included from centre 1 (mean age 61.6 ± 11.5 years, median follow up 30 months, PFS events = 57/187, OS events = 46/187) and 257 patients were included from centre 2 (mean age 62.6 ± 12.3 years, median follow up 35 months, PFS events = 70/257, OS events = 54/257). The best performing model for PFS and OS was achieved using a Cox regression model based on age and metabolic tumour volume (MTV) with a training c-index of 0.7 and an external testing c-index of 0.7 (standard error = 0.4). Conclusions A combination of patient age and MTV has been demonstrated using external validation to have the potential to predict OS and PFS in ASCC patients. Clinical relevance statement A Cox regression model using patients’ age and metabolic tumour volume showed good predictive potential for progression-free survival in external testing. The benefits of a previous radiomics model published by our group could not be confirmed on external testing. Key Points • A predictive model based on patient age and metabolic tumour volume showed potential to predict overall survival and progression-free survival and was validated on an external test cohort. • The methodology used to create a predictive model from age and metabolic tumour volume was repeatable using external cohort data. • The predictive ability of positron emission tomography-computed tomography–derived radiomic features diminished when the influence of metabolic tumour volume was accounted for.

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“…10 Applying radiomic techniques to predict CRT response in anal cancer patients enables personalized treatment, optimizing outcomes by tailoring therapy to individual characteristics. 11 Radiomic nomograms help to stratify patients, identifying those likely to respond well. 12 Predicting recurrence, survival, and disease progression aids treatment decisions, sparing high-risk patients from potentially morbid interventions.…”

Section: Introductionmentioning

confidence: 99%

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

Temperley,

O’Sullivan,

Waters

et al. 2023

The American Surgeon™

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Introduction The management of anal cancer relies on clinical and histopathological features for treatment decisions. In recent years, the field of radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving management of pelvic cancers. The aim of this study was to evaluate the current application of radiomics in the management of anal cancer. Methods A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Inclusion criteria encompassed randomized and non-randomized trials investigating the use of radiomics to predict post-operative recurrence in anal cancer. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Results The systematic review identified a total of nine studies, with 589 patients examined. There were three main outcomes assessed in included studies: recurrence (6 studies), progression-free survival (2 studies), and prediction of human papillomavirus (HPV) status (1 study). Radiomics-based risk stratification models were found to provide valuable insights into treatment response and patient outcomes, with all developed signatures demonstrating at least modest accuracy (range: .68-1.0) in predicting their primary outcome. Conclusion Radiomics has emerged as a promising tool in the management of anal cancer. It offers the potential for improved risk stratification, treatment planning, and response assessment, thereby guiding personalized therapeutic approaches.

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Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?

Cited by 3 publications

References 45 publications

MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

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