Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI

Hu, Rong; Li, Hui-Zhou; Horng, Hannah; Thomasian, Nicole M; Jiao, Zhicheng; Zhu, Chengzhang; Zou, Beiji; Bai, Harrison X.

doi:10.1038/s41598-022-11997-w

Cited by 10 publications

(6 citation statements)

References 56 publications

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“…Recent advances in machine learning brought an automatization of the model to accelerate workflow, enhance performance, and increase the accessibility of AI to clinical researchers [ 190 ]. Hu et al’s study through auto ML achieved an accuracy similar to that of manual optimization with a sensitivity and specificity comparable to that of radiologists.…”

Section: Resultsmentioning

confidence: 99%

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Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

et al. 2023

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Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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Section: Resultsmentioning

confidence: 99%

“…Hu et al’s study through auto ML achieved an accuracy similar to that of manual optimization with a sensitivity and specificity comparable to that of radiologists. However, automated ML needs to be improved on the diagnosis of LR-M of LI-RADS and needs additional features to be implemented [ 190 , 191 ].…”

Section: Resultsmentioning

confidence: 99%

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

et al. 2023

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“…Hu et al. ( 71 ) made use of a tree-based pipeline optimization tool. After manual segmentation of the tumors, different thresholds of feature selections and classifiers were evaluated.…”

Section: Ai and Diagnosismentioning

confidence: 99%

“…Shen et al (70) developed a nomogram based on LASSO and RF to identify ICC among intrahepatic lithiasis patients, with an accuracy of 82.6%. Hu et al (71) made use of a tree-based pipeline optimization tool. After manual segmentation of the tumors, different thresholds of feature selections and classifiers were evaluated.…”

Section: Ai and Diagnosismentioning

confidence: 99%

Application of AI on cholangiocarcinoma

Huang,

Bai,

Qiu

et al. 2024

Front. Oncol.

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Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

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“…Furthermore, Hu et al ( 12 ) and Miotto et al ( 13 ) have investigated the application of automated machine learning in distinguishing between types of cancers and predicting patient outcomes based on EHR data. These studies have underscored the potential of machine learning to accelerate workflow, enhance performance, and improve the accessibility of artificial intelligence in clinical research.…”

Section: Introductionmentioning

confidence: 99%

Wearable sensors in patient acuity assessment in critical care

Sena,

Mostafiz,

Zhang

et al. 2024

Front. Neurol.

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Acuity assessments are vital for timely interventions and fair resource allocation in critical care settings. Conventional acuity scoring systems heavily depend on subjective patient assessments, leaving room for implicit bias and errors. These assessments are often manual, time-consuming, intermittent, and challenging to interpret accurately, especially for healthcare providers. This risk of bias and error is likely most pronounced in time-constrained and high-stakes environments, such as critical care settings. Furthermore, such scores do not incorporate other information, such as patients’ mobility level, which can indicate recovery or deterioration in the intensive care unit (ICU), especially at a granular level. We hypothesized that wearable sensor data could assist in assessing patient acuity granularly, especially in conjunction with clinical data from electronic health records (EHR). In this prospective study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for estimating acuity. Accelerometry data were collected from 87 patients wearing accelerometers on their wrists in an academic hospital setting. The data was evaluated using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (Sequential Organ Failure Assessment, SOFA) used as a baseline when predicting acuity state (for ground truth we labeled as unstable patients if they needed life-supporting therapies, and as stable otherwise), particularly regarding the precision, sensitivity, and F1 score. The results demonstrate that integrating accelerometer data with demographics and clinical variables improves predictive performance compared to traditional scoring systems in healthcare. Deep learning models consistently outperformed the SOFA score baseline across various scenarios, showing notable enhancements in metrics such as the area under the receiver operating characteristic (ROC) Curve (AUC), precision, sensitivity, specificity, and F1 score. The most comprehensive scenario, leveraging accelerometer, demographics, and clinical data, achieved the highest AUC of 0.73, compared to 0.53 when using SOFA score as the baseline, with significant improvements in precision (0.80 vs. 0.23), specificity (0.79 vs. 0.73), and F1 score (0.77 vs. 0.66). This study demonstrates a novel approach beyond the simplistic differentiation between stable and unstable conditions. By incorporating mobility and comprehensive patient information, we distinguish between these states in critically ill patients and capture essential nuances in physiology and functional status. Unlike rudimentary definitions, such as equating low blood pressure with instability, our methodology delves deeper, offering a more holistic understanding and potentially valuable insights for acuity assessment.

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Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI

Cited by 10 publications

References 56 publications

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Application of AI on cholangiocarcinoma

Wearable sensors in patient acuity assessment in critical care

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