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<p>Cholangiocarcinoma is a heterogeneous group of highly aggressive cancers that form in the bile ducts of the liver. The risk factors associated with geographical variations include hepatolithiasis, primary sclerosing cholangitis, and liver fluke infection. Based on anatomical location, Cholangiocarcinoma is divided into three types: perihilar, intrahepatic, and distal. Early and accurate detection of Cholangiocarcinoma is a major challenge. This review article discusses the processes, treatments, and approaches for the detection of Cholangiocarcinoma using medical image processing. A comprehensive review is conducted on image acquisition and preprocessing, describing the data sources for collecting liver cancer medical images and the steps to enhance image quality and contrast. Filtering and histogram-based approaches are proposed for enhancing images by reducing noise content and smoothing images. Different segmentation approaches, such as edge-based, threshold-based, and region-based methods, are employed to accurately identify the affected regions by locating regions and boundaries of the images. Relevant features presented in the identified regions are extracted to provide higher efficiency in the detection model, reducing complexity without losing meaningful information. For the detection of Cholangiocarcinoma, the modifications performed in the machine learning and deep learning models are implemented and discussed. Various analyses are performed to diagnose Cholangiocarcinoma accurately. Treatment processes, including photodynamic therapy, drug therapy, radiation therapy, liver transplantation, surgery, and chemotherapy, are reviewed to provide guidelines on curing Cholangiocarcinoma. This review paper is suitable for identifying some measuring parameters such as tumor size and location, biomarkers, histopathological assessment, staging, imaging parameters, liver functioning and genetic features. Finally, the challenges during early detection and accurate identification of Cholangiocarcinoma are addressed, and promising solutions for these challenges are provided.</p>

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Optimizing Diagnosis Approaches: A Computational Perspective for Medical Image Processing in Cholangiocarcinoma Detection

  • 期刊论文

  • 发表日期：

    2025年9月5日

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  • Avinash Dwivedi,
  • Shivani Joshi,
  • Rajiv Kumar,
  • Vipin Rai,
  • Vikas Chaudhary,
  • Pradeep Kumar Mishra

摘要

Cholangiocarcinoma is a heterogeneous group of highly aggressive cancers that form in the bile ducts of the liver. The risk factors associated with geographical variations include hepatolithiasis, primary sclerosing cholangitis, and liver fluke infection. Based on anatomical location, Cholangiocarcinoma is divided into three types: perihilar, intrahepatic, and distal. Early and accurate detection of Cholangiocarcinoma is a major challenge. This review article discusses the processes, treatments, and approaches for the detection of Cholangiocarcinoma using medical image processing. A comprehensive review is conducted on image acquisition and preprocessing, describing the data sources for collecting liver cancer medical images and the steps to enhance image quality and contrast. Filtering and histogram-based approaches are proposed for enhancing images by reducing noise content and smoothing images. Different segmentation approaches, such as edge-based, threshold-based, and region-based methods, are employed to accurately identify the affected regions by locating regions and boundaries of the images. Relevant features presented in the identified regions are extracted to provide higher efficiency in the detection model, reducing complexity without losing meaningful information. For the detection of Cholangiocarcinoma, the modifications performed in the machine learning and deep learning models are implemented and discussed. Various analyses are performed to diagnose Cholangiocarcinoma accurately. Treatment processes, including photodynamic therapy, drug therapy, radiation therapy, liver transplantation, surgery, and chemotherapy, are reviewed to provide guidelines on curing Cholangiocarcinoma. This review paper is suitable for identifying some measuring parameters such as tumor size and location, biomarkers, histopathological assessment, staging, imaging parameters, liver functioning and genetic features. Finally, the challenges during early detection and accurate identification of Cholangiocarcinoma are addressed, and promising solutions for these challenges are provided.