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Cited by 54AI in cancer detectionRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

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

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Sadeghi, Mohammad Hossein, Sina, Sedigheh, Omidi, Hamid, Farshchitabrizi, Amir Hossein, Alavi, Mehrosadat·2024·Polish Journal of Radiology
DOI 10.5114/pjr.2024.134817PMC PMC10867948
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Abstract

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. 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. 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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  • The model's real-time capabilities allow it to be used during surgical procedures, giving doctors valuable information to guide their interventions.

    “The model's real-time capabilities allow it to be used during surgical procedures, giving doctors valuable information to guide their interventions [60].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [60]

    In Results, ‘Challenges and opportunities of deep learning in cancer diagnosis’ (page unavailable), the review says most deep-learning approaches have not been used for real-time ovarian cancer diagnosis and describes real-time use as a future possibility. It does not describe an existing model being used during surgery to guide interventions. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Sadeghi, M. H., Sina, S., Omidi, H., Farshchitabrizi, A. H., & Alavi, M.
Affiliations
  • Shiraz University (IR)
  • Shiraz University of Medical Sciences (IR)
Corresponding author
Sina, Sedigheh
ORCID
Venue
Polish Journal of Radiology
Publisher
Termedia Sp. z.o.o.
Published
2024-01-22
Volume
89
Pages
30-48
ISSN
  • 0137-7183 (ISSN-L)
  • 1899-0967
DOI
10.5114/pjr.2024.134817
PMC
PMC10867948
OpenAlex
W4391453550
Cited by
54
References
4
Citations by year
2026: 17 · 2025: 25 · 2024: 12
FWCI
1.86
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Polish Journal of Radiology (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
International Atomic Energy Agency
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
ovarian cancer, deep learning, convolutional neural networks, diagnostic imaging, computer-aided diagnosis, radiomics, tumor differentiation, diagnostic accuracy

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