Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists

Bae, Heejin; Lee, Hansang; Kim, Sung-Won; Han, Kyunghwa; Rhee, Hyungjin; Kim, Dongkyu; Kwon, Hyuk Po; Hong, Helen; Lim, Joon Seok

doi:10.1007/s00330-021-07877-y

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…Numerous large scale studies utilizing CT or MR imaging have employed radiomics to distinguish various liver lesions, yielding areas under ROC curves (AUC) ranging from 0.7 to 0.95 (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). These investigations demonstrated robust performance not only on the training set but also on testing and validation sets.…”

Section: Early Detection and Accurate Tumor Classificationmentioning

confidence: 99%

“…The scope of these studies encompassed a wide range of classification tasks and discriminating lesions, including HCC, hemangioma, cysts, adenoma, hepatic focal nodular hyperplasia, CC, combined HCC-CC, inflammatory masses, and metastasis. Clinical variables were integrated into certain models to enhance their performance (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). A multitude of AI studies has endeavored to predict liver malignancies, focusing on diverse aspects such as detecting HCC (30)(31)(32), classifying major features of LI-RADS (12, 33, 34), and discerning classic HCC form other malignant and nonmalignant liver lesions.…”

Section: Early Detection and Accurate Tumor Classificationmentioning

confidence: 99%

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Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Haghshomar,

Rodrigues,

Kalyan

et al. 2024

Front. Oncol.

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Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

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Section: Early Detection and Accurate Tumor Classificationmentioning

confidence: 99%

Section: Early Detection and Accurate Tumor Classificationmentioning

confidence: 99%

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Haghshomar,

Rodrigues,

Kalyan

et al. 2024

Front. Oncol.

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“…Wong et al For the screening selection of patients for malignancy among patients with benign hyperplasia, an ensemble method of processing radiomarkers was developed on magnetic resonance imaging data of 442 patients, which achieved 85% efficiency in separating pathologies (p = 0.05). And in the work of H. Bae et al [30], a retrospective study of 502 patients who underwent computed tomography with contrast was conducted to differentiate between cysts, haemangiomas, and colorectal metastases to the liver. The diagnostic performance of the developed model for assessing radiological features was compared with the diagnostic performance of 4 qualified radiologists.…”

Section: Key Digital Technologiesmentioning

confidence: 99%

Implementation of digital technologies in the medicine of the future

2022

FEM

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The parallel development of digital and telecommunication technologies in recent years has created ample opportunities to improve traditional healthcare delivery systems. New models of healthcare services are supported by scientific developments and digital innovations. Objectives: To improve doctors’ expertise and healthcare managers with the main achievements of digital technologies introduction in practical medicine and the possibilities for further development of technologies in this area. Methodology: A review of articles from professional peer-reviewed journals in the online medical literature search system MEDLINE from the US National Library of Medicine for 2018-2022 was conducted. The article concerns the use of digital innovations in clinical medicine, their achievements in oncology, cardiology, medical imaging and other healthcare fields. Results: The article discusses the main digital strategies that are part of the practical work of doctors and create new workflows and interactions between patients and specialists of medical institutions. Attention is paid to the advances in computer technology and the challenges they pose to the quality of medical care. The introduction of telemedicine, problems of validation of results and clinical use of artificial intelligence applications, as well as opportunities for their development in the future are discussed. Conclusion: New technologies, including artificial intelligence algorithms, machine learning, image recognition, radiomics, and telemedicine, are creating the prerequisites for the development of new forms of healthcare delivery. The combination of telemedicine, server-based applications, and machine learning algorithms can provide a synchronised solution to the challenges faced by modern society in ensuring a healthy future around the world.

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Radiomics in precision medicine for gastric cancer: opportunities and challenges

et al. 2022

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Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists

Cited by 7 publications

References 42 publications

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Implementation of digital technologies in the medicine of the future

Radiomics in precision medicine for gastric cancer: opportunities and challenges

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