Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images

Wang, Xun; Li, Hanlin; Zheng, Pan

doi:10.1155/2022/6009107

Cited by 11 publications

(6 citation statements)

References 39 publications

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“…The final model achieved the F1 score of 89.85%, mean average precision (mAP) of 74.85% for classification, mean pixel accuracy (MPA) of 92.71%, and mean intersection over union (MIoU) of 89.63% for segmentation. The model incorporates improvements over the previous YOLO-OC [ 39 ] model and includes a multi-task model for detection and segmentation tasks [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Sadeghi,

Sina,

Omidi

et al. 2024

Pol J Radiol

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Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. <br /> This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies.<br /> The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.

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Section: Resultsmentioning

confidence: 99%

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Sadeghi,

Sina,

Omidi

et al. 2024

Pol J Radiol

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“…Pre-treatment ADC histogram analysis has also been found to be a promising approach when planning chemotherapy by predicting advanced OC patients’ responses to platinum-based chemotherapy [ 27 ]. Novel tumour characterization models are also being investigated and integrative MRI- and CT-based machine learning models have been proposed [ 33 – 36 ].…”

Section: Discussionmentioning

confidence: 99%

Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer

et al. 2023

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Objectives Multiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT). Methods We retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient’s disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders. Results The interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71–0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT. Conclusion The mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC. Trial registration Retrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).

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“…Wang et al [ 4 ] used pelvic CT scan images to detect and segment out ovarian cancer tumours simultaneously, i.e., creating a multi-task deep learning model. They proposed a model called YOLO-OCv2, which was an enhancement of their previously proposed algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks

Kodipalli,

Fernandes,

Gururaj

et al. 2023

Diagnostics

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Difficulty in detecting tumours in early stages is the major cause of mortalities in patients, despite the advancements in treatment and research regarding ovarian cancer. Deep learning algorithms were applied to serve the purpose as a diagnostic tool and applied to CT scan images of the ovarian region. The images went through a series of pre-processing techniques and, further, the tumour was segmented using the UNet model. The instances were then classified into two categories—benign and malignant tumours. Classification was performed using deep learning models like CNN, ResNet, DenseNet, Inception-ResNet, VGG16 and Xception, along with machine learning models such as Random Forest, Gradient Boosting, AdaBoosting and XGBoosting. DenseNet 121 emerges as the best model on this dataset after applying optimization on the machine learning models by obtaining an accuracy of 95.7%. The current work demonstrates the comparison of multiple CNN architectures with common machine learning algorithms, with and without optimization techniques applied.

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Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images

Cited by 11 publications

References 39 publications

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer

Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks

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