A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules

Hunter, Benjamin; Chen, Mitchell; Ratnakumar, Prashanthi; Alemu, Esubalew; Logan, Andrew; Linton-Reid, Kristofer; Tong, Daniel; Senthivel, Nishanthi; Bhamani, Amyn; Bloch, Susannah; Kemp, Samuel V.; Boddy, Laura; Jain, Sejal; Gareeboo, Shafick; Rawal, Bhavin; Doran, Simon J.; Navani, Neal; Nair, Arjun; Bunce, Catey; Kaye, Stan B.; Blackledge, Matthew; Aboagye, Eric O.; Devaraj, Anand; Lee, Richard W.

doi:10.1016/j.ebiom.2022.104344

Cited by 24 publications

(17 citation statements)

References 35 publications

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“…The system may also provide simulations of different scenarios, predictive modeling, and visualization tools to help users understand the possible outcomes of their decisions. AI-based DSS has been utilized for predicting mental health disorders, recommending surgery, and diagnosing COVID-19, among other tasks [78][79][80][81][82][83][84]. With increasing clinical and imaging data on PCL subtypes collected, future studies in this field may leverage DSS as well.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Jiang

Chao

Culp

et al. 2023

Cancers

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Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65–75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Jiang

Chao

Culp

et al. 2023

Cancers

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“…These methods have achieved remarkable success, especially in disease classification and risk assessments, in several image-based disciplines, such as dermatology, gastroenterology, ophthalmology, oncology, and neuroradiology (10)(11)(12)(13)(14)(15)(16), including the development of 'omics'-based decision support tools (17)(18)(19)(20)(21). The application of radiomics to cardiac ultrasound (i.e., ultrasomics), may aid in risk stratification of patients experiencing an AMI by extracting texture-based information from the myocardium.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) techniques have led to the development of novel methods that includes subjecting images and other inputs to sophisticated algorithms to capture complexity of human health and disease at the level of the individual (10). These methods have achieved remarkable success, especially in disease classification and risk assessments, in several image-based disciplines, such as dermatology, gastroenterology, ophthalmology, oncology, and neuroradiology (10–16), including the development of ‘omics’-based decision support tools (17–21). The application of radiomics to cardiac ultrasound (i.e., ultrasomics), may aid in risk stratification of patients experiencing an AMI by extracting texture-based information from the myocardium.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Texture Analysis for Acute Myocardial Infarction Risk Stratification: A Pilot Study

Hathaway,

Jamthikar,

Chaitman

et al. 2024

Preprint

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Background: Current risk stratification tools for acute myocardial infarction (AMI) have limitations, particularly in predicting mortality. This study utilizes cardiac ultrasound radiomics (i.e., ultrasomics) to risk stratify AMI patients when predicting all cause mortality. Methods: The study included 197 patients: a) retrospective internal cohort (n=155) of non ST elevation myocardial infarction (n=63) and ST elevation myocardial infarction (n=92) patients, and b) external cohort from the multicenter Door To Unload in ST segment elevation myocardial infarction [DTU STEMI] Pilot Trial (n=42). Echocardiography images of apical 2, 3, and 4 chamber were processed through an automated deep learning pipeline to extract ultrasomic features. Unsupervised machine learning (topological data analysis) generated AMI clusters followed by a supervised classifier to generate individual predicted probabilities. Validation included assessing the incremental value of predicted probabilities over the Global Registry of Acute Coronary Events (GRACE) risk score 2.0 to predict 1 year all cause mortality in the internal cohort and infarct size in the external cohort. Results: Three phenogroups were identified: Cluster A (high risk), Cluster B (intermediate risk), and Cluster C (low risk). Cluster A patients had decreased LV ejection fraction (P=0.004) and global longitudinal strain (P=0.027) and increased mortality at 1 year (log rank P=0.049). Ultrasomics features alone (C Index: 0.74 vs. 0.70, P=0.039) and combined with global longitudinal strain (C Index: 0.81 vs. 0.70, P<0.001) increased prediction of mortality beyond the GRACE 2.0 score. In the DTU STEMI clinical trial, Cluster A was associated with larger infarcts size (>10% LV mass, P=0.003), compared to remaining clusters. Conclusions: Ultrasomics based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification.

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“…In recent years, radiomics has been gradually and widely used in the diagnosis of cancers ( 10 ), identification of molecular typing of tumors ( 11 ), prediction of survival status of patients ( 12 ), and the use of imaging genomics to analyze the relationship between imaging features and genomic features to dissect tumor heterogeneity ( 13 ). Radiomics studies targeting NETs are also increasing, and radiomics can be applied in the diagnosis of pancreatic NETs ( 14 ), predicting the grading of pancreatic NETs ( 15 ), determining the prognosis of NETs ( 16 ), and assessing the effects of drug therapy for NETs ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a contrast-enhanced CT-based radiomics nomogram for preoperative diagnosis in neuroendocrine carcinoma of digestive system

Yang

Xiang

et al. 2023

Front. Endocrinol.

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ObjectivesTo develop and validate a contrast-enhanced CT-based radiomics nomogram for the diagnosis of neuroendocrine carcinoma of the digestive system.MethodsThe clinical data and contrast-enhanced CT images of 60 patients with pathologically confirmed neuroendocrine carcinoma of the digestive system and 60 patients with non-neuroendocrine carcinoma of the digestive system were retrospectively collected from August 2015 to December 2021 at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and randomly divided into a training cohort (n=84) and a validation cohort (n=36). Clinical characteristics were analyzed by logistic regression and a clinical diagnosis model was developed. Radiomics signature were established by extracting radiomic features from contrast-enhanced CT images. Based on the radiomic signature and clinical characteristics, radiomic nomogram was developed. ROC curves and Delong’s test were used to evaluate the diagnostic efficacy of the three models, calibration curves and application decision curves were used to analyze the accuracy and clinical application value of nomogram.ResultsLogistic regression results showed that TNM stage (stage IV) (OR 6.8, 95% CI 1.320-43.164, p=0. 028) was an independent factor affecting the diagnosis for NECs of the digestive system, and a clinical model was constructed based on TNM stage (stage IV). The AUCs of the clinical model, radiomics signature, and radiomics nomogram for the diagnosis of NECs of the digestive system in the training, validation cohorts and pooled patients were 0.643, 0.893, 0.913; 0.722, 0.867, 0.932 and 0.667, 0.887, 0.917 respectively. The AUCs of radiomics signature and radiomics nomogram were higher than clinical model, with statistically significant difference (Z=4.46, 6.85, both p < 0.001); the AUC difference between radiomics signature and radiomics nomogram was not statistically significant (Z=1.63, p = 0.104). The results of the calibration curve showed favorable agreement between the predicted values of the nomogram and the pathological results, and the decision curve analysis indicated that the nomogram had favorable application in clinical practice.ConclusionsThe nomogram constructed based on contrast-enhanced CT radiomics and clinical characteristics was able to effectively diagnose neuroendocrine carcinoma of the digestive system.

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A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules

Cited by 24 publications

References 35 publications

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Ultrasonic Texture Analysis for Acute Myocardial Infarction Risk Stratification: A Pilot Study

Development and validation of a contrast-enhanced CT-based radiomics nomogram for preoperative diagnosis in neuroendocrine carcinoma of digestive system

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