Application of artificial intelligence in gynecologic malignancies: A review

Sone, Kenbun; Toyohara, Yusuke; Taguchi, Ayumi; Miyamoto, Yukio; Uchino-Mori, Mayuyo; Iriyama, Takayuki; Tsuruga, Tetsushi; Osuga, Yutaka

doi:10.1111/jog.14818

Cited by 31 publications

(18 citation statements)

References 47 publications

(74 reference statements)

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“…Cancer research [218] and especially precision medicine [219,220] benefit from the rapid development of omics technologies. High dimensionality and heterogeneity of the data often require analysis techniques beyond simple test statistics, so AI-based analyses are on the rise, particularly machine learning and deep learning methods [221][222][223][224] (Figure 6). Multi-omics studies related to µg and cancer are currently rare, but the open-access NASA GeneLab provides advanced multi-omics analysis capabilities related to spaceflight response [225].…”

Section: Multi-omics Analyses a New Perspective For Microgravity-rela...mentioning

confidence: 99%

Section: Multi-omics Analyses a New Perspective For Microgravity-rela...mentioning

confidence: 99%

See 1 more Smart Citation

The Fight against Cancer by Microgravity: The Multicellular Spheroid as a Metastasis Model

Grimm

Schulz

Krüger

et al. 2022

IJMS

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Cancer is a disease exhibiting uncontrollable cell growth and spreading to other parts of the organism. It is a heavy, worldwide burden for mankind with high morbidity and mortality. Therefore, groundbreaking research and innovations are necessary. Research in space under microgravity (µg) conditions is a novel approach with the potential to fight cancer and develop future cancer therapies. Space travel is accompanied by adverse effects on our health, and there is a need to counteract these health problems. On the cellular level, studies have shown that real (r-) and simulated (s-) µg impact survival, apoptosis, proliferation, migration, and adhesion as well as the cytoskeleton, the extracellular matrix, focal adhesion, and growth factors in cancer cells. Moreover, the µg-environment induces in vitro 3D tumor models (multicellular spheroids and organoids) with a high potential for preclinical drug targeting, cancer drug development, and studying the processes of cancer progression and metastasis on a molecular level. This review focuses on the effects of r- and s-µg on different types of cells deriving from thyroid, breast, lung, skin, and prostate cancer, as well as tumors of the gastrointestinal tract. In addition, we summarize the current knowledge of the impact of µg on cancerous stem cells. The information demonstrates that µg has become an important new technology for increasing current knowledge of cancer biology.

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Section: Multi-omics Analyses a New Perspective For Microgravity-rela...mentioning

confidence: 99%

Section: Multi-omics Analyses a New Perspective For Microgravity-rela...mentioning

confidence: 99%

The Fight against Cancer by Microgravity: The Multicellular Spheroid as a Metastasis Model

Grimm

Schulz

Krüger

et al. 2022

IJMS

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show abstract

“…In Supplementary Table S1 , we have complied recent review articles detailing emerging examples of how statistical and ML methods are being utilized for clinical outcome prediction in major medical specialities. Applications are found in the fields of Anesthesiology [ 32 , 33 , 34 ], Dermatology [ 35 , 36 , 37 ], Emergency Medicine [ 38 , 39 ], Family Medicine [ 40 , 40 ], Internal Medicine [ 41 , 42 , 43 ], Interventional Radiology [ 44 , 45 ], Medical Genetics [ 46 ], Neurological Surgery [ 47 ], Neurology [ 48 , 49 , 50 ], Obstetrics and Gynecology [ 51 , 52 ], Ophthalmology [ 53 , 54 , 55 ], Orthopaedic Surgery [ 56 ], Otorhinolaryngology [ 57 , 58 ], Pathology [ 59 , 60 , 61 ], Pediatrics [ 62 ], Physical Medicine and Rehabilitation [ 63 , 64 ], Plastic and Reconstructive Surgery [ 65 , 66 ], Psychiatry [ 67 , 68 ], Radiation Oncology [ 69 , 70 ], Radiology [ 71 , 72 ], General Surgery [ 73 , 74 ], Cardiothoracic Surgery [ 75 , 76 ], Urology [ 77 , 78 ], Vascular Surgery [ 79 , 80 ]. These papers introduce terms describing ML models as ‘supervised’ or ‘unsupervised’.…”

Section: Emerging Methods and Emerging Applicationsmentioning

confidence: 99%

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Pettit

Fullem

Cheng

et al. 2021

Emerging Topics in Life Sciences

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AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

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“…Owing to advancements in computer technology and the introduction of sequencing technology, studies have promoted our understanding of cellular and genetic changes during oncogenesis and yielded more targeted and individualized treatment choices [ 10 , 11 , 12 ]. Machine learning, a component of artificial intelligence, using computer technology to simulate human intellect, can make predictions using mathematical algorithms after being trained with data.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Zhao

Zhang

Wang

et al. 2022

Genes

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Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

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Application of artificial intelligence in gynecologic malignancies: A review

Cited by 31 publications

References 47 publications

The Fight against Cancer by Microgravity: The Multicellular Spheroid as a Metastasis Model

The Fight against Cancer by Microgravity: The Multicellular Spheroid as a Metastasis Model

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

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