Scope of Artificial Intelligence in Gastrointestinal Oncology

Goyal, Hemant; Sherazi, Syed Ali Amir; Mann, Rupinder; Gandhi, Zainab; Perisetti, Abhilash; Aziz, Muhammad; Chandan, Saurabh; Kopel, Jonathan; Tharian, Benjamin; Sharma, Neil; Thosani, Nirav

doi:10.3390/cancers13215494

Cited by 9 publications

(4 citation statements)

References 133 publications

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“…Artificial intelligence (AI) may be useful in screening, diagnosing, and treating various diseases by enabling the accurate analysis of diagnostic clinical images, the identification of therapeutic targets, and the processing large of datasets. 14 , 15 Cheung et al. developed a deep learning system to evaluate the risk of cardiovascular disease by measuring the calibre of retinal vessels, which can be used to effectively predict the risk of cardiovascular disease.…”

Section: Introductionmentioning

confidence: 99%

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study

Li¹,

Yang²,

Zhang³

et al. 2023

eClinicalMedicine

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Section: Introductionmentioning

confidence: 99%

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study

Li¹,

Yang²,

Zhang³

et al. 2023

eClinicalMedicine

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“…Artificial intelligence often demonstrates excellent processing capabilities in image processing and can simulate the human learning process through computer algorithms. In the medical field, artificial intelligence can be utilised for the analysis and diagnosis of medical images, such as detecting tumours and identifying signs of disease (44)(45)(46). In the field of skin disease diagnosis, a recent study has utilised reflectance confocal microscopy for the differential diagnosis of skin diseases, and it has shown promising performance.…”

Section: Discussionmentioning

confidence: 99%

Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework

Guan,

Yuan,

et al. 2024

J. Cancer

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This study has used machine learning algorithms to develop a predictive model for differentiating between dermoscopic images of basal cell carcinoma (BCC) and actinic keratosis (AK). We compiled a total of 904 dermoscopic images from two sources -the public dataset (HAM10000) and our proprietary dataset from the First Affiliated Hospital of Dalian Medical University (DAYISET 1) -and subsequently categorised these images into four distinct cohorts. The study developed a deep learning model for quantitative analysis of image features and integrated 15 machine learning algorithms, generating 207 algorithmic combinations through random combinations and cross-validation. The final predictive model, formed by integrating XGBoost with Lasso regression, exhibited effective performance in the differential diagnosis of BCC and AK. The model demonstrated high sensitivity in the training set and maintained stable performance in three validation sets. The area under the curve (AUC) value reached 1.000 in the training set and an average of 0.695 in the validation sets. The study concludes that the constructed discriminative diagnostic model based on machine learning algorithms has excellent predictive capabilities that could enhance clinical decision-making efficiency, reduce unnecessary biopsies, and provide valuable guidance for further treatment.

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“…In recent days, CNN research on various malignancies is in its early stages, to reduce the stress of medical work and improve the utilization of medical resources through artificial intelligence technology (36)(37)(38). Several intelligent systems based on CT images or pathological images have been tested in GC employing deep learning network technology.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer

Zeng

Zhu

et al. 2022

Front. Med.

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BackgroundThis study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC).Materials and methodsThis study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared.ResultsWe constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847–0.956) and 0.915 (95% CI: 0.850–0.981) in the internal validation and external validation cohorts, respectively.ConclusionWe first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

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Scope of Artificial Intelligence in Gastrointestinal Oncology

Cited by 9 publications

References 133 publications

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study

Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer

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