Review of Cervix Cancer Classification Using Radiomics on Diffusion-Weighted Imaging

Aouadi, Souha; Mohamed, Nadin; Aloudeh, Jude; Zeid, Mohamed; Bouhali, O.; Hammoud, R.; Al-Hammadi, Noora; Torfeh, Tarraf

doi:10.5772/intechopen.107497

Cited by 1 publication

(1 citation statement)

References 79 publications

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“…When performing a search on Pubmed database with the key words (deep learning) AND (cervix cancer) AND (Diffusion weighted imaging), only three publications were found that investigated DL for tumor segmentation [31,32] or the prediction of normal-sized lymph node metastasis in CC [33]. On the other hand, the use of radiomics analysis was more popular due to possible interpretation of the features in comparison to DL ones [34]. Only three studies were found to rely on DWI/ADC as input of the machine learning model [8][9][10].…”

Section: Discussionmentioning

confidence: 99%

Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet

Aouadi,

Torfeh,

Bouhali

et al. 2024

Biomed. Phys. Eng. Express

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Purpose: This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) mapping using deep convolutional neural networks (DCNN).Methods: datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining patients with no reported grade were retrospectively collected. The study was IRB approved. For each patient, sagittal and axial slices containing the gross tumor volume (GTV) were extracted from ADC maps. These were computed using the mono exponential model from diffusion weighted images (b-values =0, 100, 1000) that were acquired prior to radiotherapy treatment. Balanced training sets were created using the Synthetic Minority Oversampling Technique (SMOTE) and fed to the DCNN. EfficientNetB0 and EfficientNetB3 were transferred from the ImageNet application to binary and four-class classification tasks. Five-fold stratified cross validation was performed for the assessment of the networks. Multiple evaluation metrics were computed including the area under the receiver operating characteristic curve (AUC). Comparisons with Resnet50, Xception, and radiomic analysis were performed.Results: for grade prediction, EfficientNetB3 gave the best performance with AUC=0.924. For stage prediction, EfficientNetB0 was the best with AUC=0.931. The difference between both models was, however, small and not statistically significant EfficientNetB0-B3 outperformed ResNet50 (AUC=0.71) and Xception (AUC=0.89) in stage prediction, and demonstrated comparable results in grade classification, where AUCs of 0.89 and 0.90 were achieved by ResNet50 and Xception, respectively. DCNN outperformed radiomic analysis that gave AUC=0.67 (grade) and AUC=0.66 (stage).Conclusion: the prediction of CC grade and stage from ADC maps is feasible by adapting EfficientNet approaches to the medical context. 

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