Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

Yang, Chun Mei; Shu, Jiwu

doi:10.1159/000507449

Cited by 22 publications

(15 citation statements)

References 103 publications

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“…With the development of precision medicine and artificial intelligence, radiomics has appeared as a new medical field that is able to capture and quantify lesion heterogeneity, especially for lesions in oncology patients, by extracting and analyzing the quantitative texture features (39). Importantly, we have made great efforts and obtained some amazing results about radiomics analysis in recent years (40)(41)(42). In this study, we first extracted 396 radiomics features from each ALN in the CECT images, and used all of the radiomics features to develop the radiomics model for discriminating ALN status in patients with breast cancer based on the SVM algorithm.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

et al. 2021

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BackgroundThe use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.PurposeThe aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.MethodsWe retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).ResultsThe radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.ConclusionThe radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

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Section: Discussionmentioning

confidence: 99%

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

et al. 2021

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“…Although the gold standard of diagnosis and grading for CCA is still pathological examination, radiology (figure 5) has a pivotal role in the management of CCA in terms of diagnosis, staging, follow-up and response to therapy 159 160. Some radiological findings at CT and MRI, such as capsular retraction and the presence of a homogeneous mass with rim-like enhancement and progressive contrast uptake, are highly indicative of a CCA diagnosis.…”

Section: Radiologymentioning

confidence: 99%

Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions

Macı́as

Cardinale

Kendall

et al. 2022

Gut

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Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.

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“…Histologic examination was the most common confirmatory diagnostic modality (77.5%), followed by a much lower rate of radiologic imaging. Different imaging modalities are available for diagnosis and staging, such as ultrasound (contrast-enhanced, intraductal, and endoscopic), CT, positron emission tomography (PET), MRI, and direct cholangiography [10]. Endoscopic ultrasonography (EUS) is increasingly recognized as an important diagnostic tool for pancreaticobiliary malignancies that evaluates duct morphology, regional lymphadenopathy, and vascular involvement and facilitates tissue acquisition [11].…”

Section: Diagnostic Confirmationmentioning

confidence: 99%

Trends of the Extra-Hepatic Biliary Cancer and Its Surgical Management: A Cross-Sectional Study From the National Cancer Database

Tuma¹,

Abbaszadeh-Kasbi²,

Munene³

et al. 2022

Cureus

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IntroductionBiliary cancers are rare cancers with poor prognoses. In this study, we aimed to evaluate trends in early detection and surgical treatment and approaches in extra-hepatic biliary tract cancers (EBCs) over 13 years in the US. MethodsThe most recent data on patients diagnosed with EBC between 2004 and 2016 were extracted from the National Cancer Database (NCDB). The patients' demographics (sex, age, race), primary tumor sites, tumor grades and stages, staging modalities, diagnostic confirmation, surgical treatment modalities and approaches, and 90-day mortality were analyzed to determine trends. ResultsBiopsy was the most common staging modality in 63.9% of total 60,291 patients. The bile duct was the primary tumor site (55.0%). Histologic examination was the most common confirmatory diagnostic modality (77.5%). The most common stage was stage II (23%). The most common surgical treatment modality was radical surgery (13.88%). The open surgical approach was used in 27.1% of patients, followed by a laparoscopic approach (4.3%). ConclusionEBC showed no significant change in the trends of the stage at diagnosis, treatment modality, and extent of surgical procedures despite advances in surgical diagnostic and therapeutic modalities; however, the total number of cases slightly increased between 2004 and 2016.

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Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

Cited by 22 publications

References 103 publications

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions

Trends of the Extra-Hepatic Biliary Cancer and Its Surgical Management: A Cross-Sectional Study From the National Cancer Database

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