Performance of a deep learning-based identification system for esophageal cancer from CT images

Takeuchi, Masashi; Seto, Toshiki; Hashimoto, Masahiro; Ichihara, Nao; Morimoto, Yosuke; Kawakubo, Hirofumi; Suzuki, Tomohiko; Jinzaki, Masahiro; Kitagawa, Yuko; Matsubara, Hiroaki; Sakakibara, Yasubumi

doi:10.1007/s10388-021-00826-0

Cited by 33 publications

(24 citation statements)

References 24 publications

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“…Using an automatic tube current modulation technique (Dose-Right; Philips Medical Systems), effective mAs ranged from 69 to 379 mAs. Transverse and coronal section datasets were reconstructed with 4-mm thick sections at 3-mm increments Segmentation: ROI First-order statistics, second-order GLCM statistics Wang [ 22 ] Pretreatment PP CT Chest unenhanced CT scans were acquired with 0.625 mm collimation, 120–140 kVp, and 300–350 mAs Least squares SVM modeling MATLAB Takeuchi [ 20 ] Pretreatment PP CT Tube voltage, 120 kVp; tube current, 100–750 mA; and pitch, 1.375:1 CNN-based model using training Foley [ 21 ] Pretreatment PP CT CT images were acquired in a helical acquisition with a pitch of 0.98 and tube rotation speed of 0.5 s. Tube output was 120 kVp with output modulation between 20 and 200 mA. Matrix size for the CT acquisition was 512 × 512 pixels with a 50-cm field of view ATLAAS segmentation Rishi, 2020 Pretreatment PP CT Image resolution was 128 9 128 pixels, with voxel dimensions of 5.47 9 5.47 9 3.27 mm, and slice thickness of 3.27 mm.…”

Section: Resultsmentioning

confidence: 99%

“…Takeuchi et al reported diagnostic accuracy of 84% (sensitivity 71.7%; specificity 90.0%) in detecting stage T1–T5 esophageal cancer in 46 patients. 20 One study looked at the prognosis of patients with esophageal cancer, in which Foley et al reported six variables to be predictive of overall survival in their work of 405 patients. 21 Two studies evaluated the use of ML to assess response to chemoradiotherapy for esophageal cancers.…”

Section: Resultsmentioning

confidence: 99%

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Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy

et al. 2021

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Background Upper gastrointestinal cancers are aggressive malignancies with poor prognosis, even following multimodality therapy. As such, they require timely and accurate diagnostic and surveillance strategies; however, such radiological workflows necessitate considerable expertise and resource to maintain. In order to lessen the workload upon already stretched health systems, there has been increasing focus on the development and use of artificial intelligence (AI)-centred diagnostic systems. This systematic review summarizes the clinical applicability and diagnostic performance of AI-centred systems in the diagnosis and surveillance of esophagogastric cancers. Methods A systematic review was performed using the MEDLINE, EMBASE, Cochrane Review, and Scopus databases. Articles on the use of AI and radiomics for the diagnosis and surveillance of patients with esophageal cancer were evaluated, and quality assessment of studies was performed using the QUADAS-2 tool. A meta-analysis was performed to assess the diagnostic accuracy of sequencing methodologies. Results Thirty-six studies that described the use of AI were included in the qualitative synthesis and six studies involving 1352 patients were included in the quantitative analysis. Of these six studies, four studies assessed the utility of AI in gastric cancer diagnosis, one study assessed its utility for diagnosing esophageal cancer, and one study assessed its utility for surveillance. The pooled sensitivity and specificity were 73.4% (64.6–80.7) and 89.7% (82.7–94.1), respectively. Conclusions AI systems have shown promise in diagnosing and monitoring esophageal and gastric cancer, particularly when combined with existing diagnostic methods. Further work is needed to further develop systems of greater accuracy and greater consideration of the clinical workflows that they aim to integrate within.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy

et al. 2021

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“…Similarly, CA has been applied to radiological techniques. In a recent study by Takeuchi and colleagues, the AI-based diagnostic system succeeded at analyzing CT images in detecting esophageal cancer with an 85% diagnostic accuracy [ 19 ]. Furthermore, AI has shown to be effective in the automated segmentation of the esophagus for radiotherapy [ 20 ] and for predicting histopathological features of aggressiveness of esophageal lesions by evaluating metabolic activity assessed via PET-CT [ 21 ].…”

Section: Upper Gastro-intestinal Tractmentioning

confidence: 99%

Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

et al. 2021

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The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.

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Section: Machine Learning—basic Concepts Specific Applications and Future Directions In Geamentioning

confidence: 99%

“…In addition, and relevant to potential therapeutic de-escalation therapy and patient surveillance, CT scans may also be used to monitor responses to (neoadjuvant) chemotherapy in GEA [158,159]. Other attempts involved training a model to aid the detection of GEA using CT scans [160]. Liu et al followed an integrative approach of combining preoperative biomarkers including tissue biopsies, tumor markers, and CT image objects to predict lymph node metastasis in GEA by applying regression analysis and combined this to a multivariate model [161].…”

Section: Radiology-based Approachesmentioning

confidence: 99%

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Klein

Duda

2021

Cancers

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Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

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Performance of a deep learning-based identification system for esophageal cancer from CT images

Cited by 33 publications

References 24 publications

Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy

Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy

Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

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