Deep Interactive Learning-based ovarian cancer segmentation of H&amp;E-stained whole slide images to study morphological patterns of BRCA mutation

Ho, David Joon; Chui, M. Herman; Vanderbilt, Chad; Park, Chan-Sik; Roh, Jin; Fuchs, Thomas J.

doi:10.1016/j.jpi.2022.100160

Cited by 12 publications

(3 citation statements)

References 52 publications

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“…High-grade serous ovarian cancer patches were used to train the model. In conclusion, deep interactive learning offers a solution to reduce manual annotation time in cancer diagnosis, enhancing efficiency and the potential for classification of cancer subtypes [ 83 ].…”

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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show abstract

“…Notably, a DL model can be employed to identify gene mutations by analyzing the H&E-stained pathological images of tumors. Ho et al ( 98 ) utilized DL to analyze the WSI of patients with OC and developed a model that could predict the mutation status of the BRCA gene mutation in HGSOC. These studies demonstrate the potential of DL based on WSI in quantifying tumor histopathological features and related gene behavior.…”

Section: Ai In the Radiomics Of Ocmentioning

confidence: 99%

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Wang,

Lin,

Zhuang

et al. 2024

Oncol Rep

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Artificial intelligence (AI) has emerged as a crucial technique for extracting high-throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI-based radiomics has proven to be a non-invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI-based multi-omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

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“…Wang et al [ 24 ] employed DCNN and transfer learning techniques to achieve high accuracy in distinguishing between benign, borderline, and malignant ovarian ultrasound images. David et al [ 25 ] proposed a pixel-level cancer segmentation model for ovarian cancer using DMMN [ 26 ], and introduced Deep Interactive Learning. However, due to the locality of convolutional operations, convolutional neural networks may not effectively learn global and long-term semantic information interactions.…”

Section: Introductionmentioning

confidence: 99%

PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation

Li,

He,

Guo

et al. 2024

PLoS ONE

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Ovarian cancer is a highly lethal malignancy in the field of oncology. Generally speaking, the segmentation of ovarian medical images is a necessary prerequisite for the diagnosis and treatment planning. Therefore, accurately segmenting ovarian tumors is of utmost importance. In this work, we propose a hybrid network called PMFFNet to improve the segmentation accuracy of ovarian tumors. The PMFFNet utilizes an encoder-decoder architecture. Specifically, the encoder incorporates the ViTAEv2 model to extract inter-layer multi-scale features from the feature pyramid. To address the limitation of fixed window size that hinders sufficient interaction of information, we introduce Varied-Size Window Attention (VSA) to the ViTAEv2 model to capture rich contextual information. Additionally, recognizing the significance of multi-scale features, we introduce the Multi-scale Feature Fusion Block (MFB) module. The MFB module enhances the network’s capacity to learn intricate features by capturing both local and multi-scale information, thereby enabling more precise segmentation of ovarian tumors. Finally, in conjunction with our designed decoder, our model achieves outstanding performance on the MMOTU dataset. The results are highly promising, with the model achieving scores of 97.24%, 91.15%, and 87.25% in mACC, mIoU, and mDice metrics, respectively. When compared to several Unet-based and advanced models, our approach demonstrates the best segmentation performance.

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Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

Cited by 12 publications

References 52 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

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation

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