What question did this study set out to answer?

The study aims to develop a machine learning model to screen CKD patients for undiagnosed multiple myeloma using routine lab tests.

April 5, 2026

Abstract 2716: Machine learning-based screening tool for multiple myeloma detection in chronic kidney disease patients with limited laboratory testing.

Key Result

An XGBoost machine learning model using routine laboratory data identified multiple myeloma in chronic kidney disease patients with 95.1% accuracy and a ROC-AUC of 0.962.

Key Points

The study aims to develop a machine learning model to screen CKD patients for undiagnosed multiple myeloma using routine lab tests.
Analyzed 4,759 CKD patients, including 591 with MM and 4,168 controls.
Engineered features from CBC, biochemistry, and urine studies based on routine tests.
Compared XGBoost, Random Forest, and Logistic Regression using cross-validation methods.
XGBoost achieved 95.1% accuracy and high area under the curve metrics.
The minimal 4-feature model maintained clinically useful performance with an F1 score of 0.596.
Top predictors included hemoglobin/WBC ratio and platelet count, indicating potential for accurate MM identification.

Structured PICO

Does a machine learning model using routine laboratory parameters accurately detect multiple myeloma in patients with chronic kidney disease?

Population

4,759 chronic kidney disease (CKD) patients (591 with multiple myeloma, 4,168 non-MM controls; 12.4% prevalence) from a tertiary center.

Intervention

Machine learning models (XGBoost, Random Forest, Logistic Regression) using routine laboratory parameters (CBC, biochemistry, urine studies) to detect multiple myeloma.

Outcome

Diagnostic performance (accuracy, F1-score, ROC-AUC, PR-AUC) for detecting multiple myeloma.surrogate

A machine learning model using routine laboratory data can accurately identify multiple myeloma in patients with chronic kidney disease, potentially reducing diagnostic delays.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Abstract Background: Multiple myeloma (MM) and chronic kidney disease (CKD) share similar clinical manifestations including renal impairment and anemia, making MM diagnosis challenging in general and nephrology clinics. Many CKD patients harbor undiagnosed MM, but healthcare insurance restrictions and public health system constraints limit ordering of specialized tests (serum protein electrophoresis, free light chains, immunoglobulins). This diagnostic delay can result in irreversible renal damage and delayed treatment. We developed a machine learning model using routine laboratory parameters to identify CKD patients requiring MM-specific workup or hematology referral. Methods: We analyzed 4,759 CKD patients (591 MM, 4,168 non-MM controls; 12.4% prevalence) from a tertiary center. Using only routine tests available in general practice, we engineered features including CBC (hemoglobin, WBC, platelets), biochemistry (total protein, albumin, calcium, LDH, creatinine), and urine studies (protein, albumin, creatinine). Three feature sets were tested: RANK1 (20 features), RANK2 (16 features), and RANK3 (4 minimal features). We compared XGBoost, Random Forest, and Logistic Regression using stratified 5-fold cross-validation. with both unbalanced and SMOTEENN-resampled data. Results: XGBoost achieved optimal performance: accuracy 95.1%, F1-score 0.798, ROC-AUC 0.962, PR-AUC 0.889. The minimal 4-feature model (platelet, WBC, urine protein, LDH) maintained clinically useful performance (F1 0.596, PR-AUC 0.627). SHAP analysis revealed hemoglobin/WBC ratio, platelet count, albumin/total protein ratio, and total protein as top predictors, capturing MM's characteristic hematologic suppression and paraproteinemia without requiring specialized assays. Dimensionality reduction visualization (PCA, UMAP, t-SNE) demonstrated distinct clustering patterns between MM and non-MM CKD patients, confirming inherent feature space separability despite overlapping clinical presentations. Conclusions: This ML-based screening tool demonstrates potential for identifying high-risk CKD patients warranting MM-specific testing using only routine laboratory data. However, this retrospective single-center study has limitations including potential selection bias and lack of external validation. Prospective multicenter validation studies are needed to assess real-world clinical utility, optimal decision thresholds, and impact on diagnostic timeliness before implementation in clinical practice. Citation Format: Wen-Chi Wu, Muh-Hwa Yang, Chih-Yu Yang, . Machine learning-based screening tool for multiple myeloma detection in chronic kidney disease patients with limited laboratory testing abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2716.

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Cite This Study

Wu et al. (Fri,) reported a other. An XGBoost machine learning model using routine laboratory data identified multiple myeloma in chronic kidney disease patients with 95.1% accuracy and a ROC-AUC of 0.962.

synapsesocial.com/papers/69d1fd29a79560c99a0a2f82 https://doi.org/https://doi.org/10.1158/1538-7445.am2026-2716

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