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Updated: Jun 2, 2026

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Radiomics-Based Machine Learning versus the MAP Score for Predicting Haematocrit Decline after Laparoscopic Partial

Yusuf Dogan1, Ahmet Alper Ozdes2, Mustafa Yildirim3

  • 1Department of Radiology, Etlik City Hospital, Ankara, Turkey.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP
|June 1, 2026
PubMed
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This summary is machine-generated.

Radiomics machine learning models significantly outperformed the Mayo Adhesive Probability score in predicting postoperative hematocrit reduction after laparoscopic partial nephrectomy for renal cell carcinoma.

Area of Science:

  • Urology
  • Radiology
  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Renal cell carcinoma (RCC) is a common malignancy.
  • Laparoscopic partial nephrectomy (LPN) is a standard treatment for localized RCC.
  • Predicting postoperative hematocrit (Hct) reduction is crucial for patient management.

Purpose of the Study:

  • To compare the predictive accuracy of the Mayo Adhesive Probability (MAP) score and radiomics-based machine learning models for postoperative Hct reduction after LPN for RCC.
  • To evaluate the utility of preoperative computed tomography (CT) images in predicting Hct decline.

Main Methods:

  • Retrospective observational study involving 53 RCC patients undergoing LPN.

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  • Patients were categorized into high (>6 units) and low Hct decline groups.
  • MAP scores were assigned, and 107 radiomics features were extracted from CT images.
  • Seven machine learning classifiers were trained and validated using 10-fold cross-validation.

    • Main Results:

    • The MAP score achieved an AUC of 0.777 for predicting high Hct decline.
    • The radiomics-based Naive Bayes model demonstrated superior performance with an AUC of 0.925, 95.0% accuracy, 90.0% sensitivity, and 100.0% specificity.

    Conclusions:

    • Radiomics analysis combined with machine learning significantly outperforms the MAP score in predicting postoperative Hct decline after LPN for RCC.
    • Integrating radiomics into preoperative assessment can optimize surgical planning and enhance patient safety.
    • Further multicenter prospective studies are recommended before clinical implementation.