Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

Lu, Jiaqi; Liu, Ruiqing; Zhang, Yuejuan; Zhang, Xianxiang; Zheng, Longbo; Zhang, Chao; Zhang, Kaiming; Li, Shuai; Lu, Yun

doi:10.1016/j.imed.2021.08.003

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…Imaging examination is a commonly used method to detect changes of pathological tissue structure and blood perfusion in clinical disease diagnosis. [60][61][62][63] AI has been applied on different scale databases with probabilistic and statistical methods for imaging field. Imaging feature processing and ML-based classification or prediction have great potential in helping radiologists make diagnoses as accurate as possible to reduce diagnostic time and cost.…”

Section: Off-body Detection Via Imagementioning

confidence: 99%

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Chen

Cui

Haick

et al. 2023

Advanced Sensor Research

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The aging population and the prevalence of infectious diseases have impacted the traditional medical order, significantly increasing the burden on healthcare and adversely affecting the socio‐economic system. Medical sensors based on artificial intelligence (AI) provide new ideas for modern medical data collection to monitor the health status of individuals and environmental changes. Meanwhile, with the aid of AI algorithms, the big data processing capabilities of sensor systems have been greatly improved, further realizing early predictions and timely diagnoses. In this paper, a brief overview is offered on the development status of AI‐enabled medical sensors for off‐body detection, near‐body monitoring, disease prediction, and clinical decision support system, and the ongoing challenges and future prospects to move from concept to implementation are discussed. In the foreseeable future, breakthroughs in the combination of medical sensors and AI algorithms are expected to pave the way for early detection and clinical decision support and improve the accuracy and efficiency for disease diagnosis.

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Section: Off-body Detection Via Imagementioning

confidence: 99%

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Chen

Cui

Haick

et al. 2023

Advanced Sensor Research

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“…Alternatively, Lu and colleagues [46] employed the Faster RCNN model to create a recognition framework for colorectal cancer tumor sprouting. The model can automatically identify the budding areas from pathological sections and count their numbers in a short time, with high accuracy.…”

Section: Oncologymentioning

confidence: 99%

Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

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“…Currently, AI models in colorectal cancer mainly focus on gland segmentation, tumor classification, tumor microenvironment characterization, and prognosis prediction. 7,[9][10][11][12] Hüneburg and other researchers utilized AI for realtime application in Lynch syndrome patients, revealing that AI-assisted colonoscopy optimizes endoscopic surveillance in lynch syndrome patients, notably enhancing the detection of flat adenomas. 13 In a large-scale study conducted in asymptomatic populations in China, Xu et al compared AI-assisted colonoscopy with traditional methods, indicating a substantial improvement in overall adenoma detection rates with AI assistance.…”

Section: Introductionmentioning

confidence: 99%

“…AI is a hot research area in the field of pathological diagnosis. Currently, AI models in colorectal cancer mainly focus on gland segmentation, tumor classification, tumor microenvironment characterization, and prognosis prediction 7,9–12 …”

Section: Introductionmentioning

confidence: 99%

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

Tan,

Liu,

Xue

et al. 2024

Cancer Medicine

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ObjectiveThis retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin–Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI‐assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments.MethodsThis study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA).ResultsA total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model.ConclusionThe combined model and nomogram, which integrate pathomics multi‐modal data and clinical‐pathological variables, demonstrated superior performance in distinguishing between Stage I–II and Stage III colorectal cancer. This provides valuable support for clinical decision‐making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

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Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

Cited by 10 publications

References 16 publications

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Clinical Applications of Artificial Intelligence—An Updated Overview

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

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