External validation of a radiomic signature to predict p16 (HPV) status from standard CT images of anal cancer patients

Walsh, Seán; Vaidyanathan, Akshayaa; Aliboni, Lorenzo; Sanchez, Victoria Lopez; Leech, Michelle; Joyce, Ronan; Gillham, Charles; Kridelka, Frédéric; Hustinx, Roland; Danthine, Denis; Occhipinti, Mariaelena; Vos, Wim; Guiot, Julien; Lambin, Philippe; Lovinfosse, Pierre

doi:10.1038/s41598-023-34162-3

Cited by 3 publications

(9 citation statements)

References 27 publications

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“…From these twelve full texts, a total of nine studies met the inclusion criteria and were included in our analysis. [16][17][18][19][20][21][22][23][24] See Figure 1.…”

Section: Search Resultsmentioning

confidence: 99%

“…Overall, six of the included studies were retrospective in nature, [16][17][18]21,22,24 with the remaining three being prospective. 19,20,23 In total, four studies were multicentre, 17,20,21,25 with the other five being singlecenter studies. 16,18,19,22,23 The total number of participants from the nine included studies was 589.…”

Section: Methodological Characteristics Quality Of Studies Patient Ch...mentioning

confidence: 99%

“…16,18,19,[22][23][24] The remaining studies developed their models from T2-weighted MRI (2/9) or contrast-enhanced CT (1/9). 17,20,21 Acquisition parameters are illustrated in Table 2.…”

Section: Acquisition Parametersmentioning

confidence: 99%

“…(i) Recurrence (6/9 studies) 19,20,[22][23][24] (ii) Progression-free survival (2/9 studies) 16,18 (iii) Prediction of human papillomavirus (HPV) status (1/9 studies) 21 Disease recurrence. Owczarcyk et al assessed the potential of image heterogeneity analysis of standard care MRI in predicting the outcomes of CRT in patients diagnosed with ASCC.…”

Section: Primary Outcomesmentioning

confidence: 99%

“…Prediction of p16 (HPV) Status in Anal Cancer. Prediction of p16 (HPV) status was assessed by Leijenaar et al 21 Their paper focused on evaluating the performance of an existing CT-based radiomic signature, originally designed for oropharyngeal cancer, to predict HPV status in the context of anal cancer. Their dataset was made up of 59 patients from two different medical centers.…”

Section: Primary Outcomesmentioning

confidence: 99%

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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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“…From these twelve full texts, a total of nine studies met the inclusion criteria and were included in our analysis. [16][17][18][19][20][21][22][23][24] See Figure 1.…”

Section: Search Resultsmentioning

confidence: 99%

Section: Methodological Characteristics Quality Of Studies Patient Ch...mentioning

confidence: 99%

“…16,18,19,[22][23][24] The remaining studies developed their models from T2-weighted MRI (2/9) or contrast-enhanced CT (1/9). 17,20,21 Acquisition parameters are illustrated in Table 2.…”

Section: Acquisition Parametersmentioning

confidence: 99%

Section: Primary Outcomesmentioning

confidence: 99%

Section: Primary Outcomesmentioning

confidence: 99%

See 3 more Smart Citations

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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A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples

Yao

Zhang

2023

Front. Microbiol.

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Human papillomavirus (HPV) is a sexually transmitted virus. Cervical cancer is one of the highest incidences of cancer, almost all patients are accompanied by HPV infection. In addition, the occurrence of a variety of cancers is also associated with HPV infection. HPV vaccination has gained widespread popularity in recent years with the increase in public health awareness. In this context, HPV testing not only needs to be sensitive and specific but also needs to trace the source of HPV infection. Through machine learning and deep learning, information from medical examinations can be used more effectively. In this review, we discuss recent advances in HPV testing in combination with machine learning and deep learning.

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Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis

Bicci,

Calamandrei,

Di Finizio

et al. 2024

Diagnostics

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The aim of this work is to identify MRI texture features able to predict the response to radio-chemotherapy (RT-CHT) in patients with naso-oropharyngeal carcinoma (NPC-OPC) before treatment in order to help clinical decision making. Textural features were derived from ADC maps and post-gadolinium T1-images on a single MRI machine for 37 patients with NPC-OPC. Patients were divided into two groups (responders/non-responders) according to results from MRI scans and 18F-FDG-PET/CT performed at follow-up 3–4 and 12 months after therapy and biopsy. Pre-RT-CHT lesions were segmented, and radiomic features were extracted. A non-parametric Mann–Whitney test was performed. A p-value < 0.05 was considered significant. Receiver operating characteristic curves and area-under-the-curve values were generated; a 95% confidence interval (CI) was reported. A radiomic model was constructed using the LASSO algorithm. After feature selection on MRI T1 post-contrast sequences, six features were statistically significant: gldm_DependenceEntropy and DependenceNonUniformity, glrlm_RunEntropy and RunLengthNonUniformity, and glszm_SizeZoneNonUniformity and ZoneEntropy, with significant cut-off values between responder and non-responder group. With the LASSO algorithm, the radiomic model showed an AUC of 0.89 and 95% CI: 0.78–0.99. In ADC, five features were selected with an AUC of 0.84 and 95% CI: 0.68–1. Texture analysis on post-gadolinium T1-images and ADC maps could potentially predict response to therapy in patients with NPC-OPC who will undergo exclusive treatment with RT-CHT, being, therefore, a useful tool in therapeutical–clinical decision making.

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External validation of a radiomic signature to predict p16 (HPV) status from standard CT images of anal cancer patients

Cited by 3 publications

References 27 publications

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

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

A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples

Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis

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