Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed...

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Veröffentlicht in:	International journal of cancer Jg. 155; H. 10; S. 1832 - 1845
Hauptverfasser:	Moro, Francesca, Ciancia, Marianna, Zace, Drieda, Vagni, Marica, Tran, Huong Elena, Giudice, Maria Teresa, Zoccoli, Sofia Gambigliani, Mascilini, Floriana, Ciccarone, Francesca, Boldrini, Luca, D'Antonio, Francesco, Scambia, Giovanni, Testa, Antonia Carla
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 15.11.2024 Wiley Subscription Services, Inc
Schlagworte:	Artificial Intelligence Cervical cancer Diagnosis Endometrial cancer Endometrial Neoplasms - diagnostic imaging Endometrial Neoplasms - pathology Female Genital Neoplasms, Female - diagnostic imaging Genital Neoplasms, Female - pathology Gynecological cancer Gynecology Gynecology - methods Humans Image processing Machine Learning Malignancy Oncology Ovarian cancer Ovarian Neoplasms - diagnostic imaging Ovarian Neoplasms - pathology Radiomics Systematic review tumors Ultrasonic imaging ultrasonography Ultrasonography - methods Ultrasound Uterine cancer Uterine Cervical Neoplasms - diagnostic imaging Uterine Cervical Neoplasms - pathology gynecology tumors machine learning ultrasonography artificial intelligence
ISSN:	0020-7136, 1097-0215, 1097-0215
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Abstract	The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS‐AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open‐source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression‐free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. What's new? Artificial intelligence is increasingly used in advanced medicine and is now being applied to ultrasound imaging in gynecological oncology. However, a deeper understanding of the capabilities and limitations of AI would help improve the management of cancer patients from diagnosis to treatment. The current evidence analyzed in this systematic review of 50 studies supports the role of AI as a complementary research and clinical tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. AI applications are however still largely lacking for pathologies other than ovarian cancer.
AbstractList	The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS‐AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open‐source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression‐free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. What's new? Artificial intelligence is increasingly used in advanced medicine and is now being applied to ultrasound imaging in gynecological oncology. However, a deeper understanding of the capabilities and limitations of AI would help improve the management of cancer patients from diagnosis to treatment. The current evidence analyzed in this systematic review of 50 studies supports the role of AI as a complementary research and clinical tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. AI applications are however still largely lacking for pathologies other than ovarian cancer.
Author	Scambia, Giovanni Boldrini, Luca Tran, Huong Elena Mascilini, Floriana Ciancia, Marianna Zoccoli, Sofia Gambigliani Vagni, Marica Giudice, Maria Teresa D'Antonio, Francesco Moro, Francesca Testa, Antonia Carla Zace, Drieda Ciccarone, Francesca
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38989809$$D View this record in MEDLINE/PubMed
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Snippet	The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and...
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SubjectTerms	Artificial Intelligence Cervical cancer Diagnosis Endometrial cancer Endometrial Neoplasms - diagnostic imaging Endometrial Neoplasms - pathology Female Genital Neoplasms, Female - diagnostic imaging Genital Neoplasms, Female - pathology Gynecological cancer Gynecology Gynecology - methods Humans Image processing Machine Learning Malignancy Oncology Ovarian cancer Ovarian Neoplasms - diagnostic imaging Ovarian Neoplasms - pathology Radiomics Systematic review tumors Ultrasonic imaging ultrasonography Ultrasonography - methods Ultrasound Uterine cancer Uterine Cervical Neoplasms - diagnostic imaging Uterine Cervical Neoplasms - pathology
Title	Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fijc.35092 https://www.ncbi.nlm.nih.gov/pubmed/38989809 https://www.proquest.com/docview/3129105956 https://www.proquest.com/docview/3078718915
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