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Background <p>Diabetic peripheral neuropathy (DPN) affects 20%–50% of patients with type 2 diabetes mellitus (T2DM), yet early screening is limited by restricted access to nerve conduction studies (NCS). We aimed to develop and validate a dual-modality ultrasound radiomics model combining B-mode ultrasound (BMUS) and shear wave elastography (SWE) of the medial gastrocnemius (MG) - an accessible surrogate reflecting neurogenic muscle atrophy - for noninvasive DPN detection.</p> Methods <p>A total of 190 T2DM patients (380 limbs) enrolled between June 2025 and February 2026 were allocated to training (158 limbs), internal test (68 limbs), and external validation (154 limbs) cohorts. DPN was defined as abnormal nerve conduction in at least two peripheral nerves, per ADA criteria. MG BMUS and SWE images were acquired bilaterally by blinded ultrasonographers. Sequential ICC (≥ 0.75), Mann-Whitney U (<i>P</i> &lt; 0.05), Spearman (|r| &gt; 0.7), and elastic-net filtering of 1,888 features selected 14 final features (9 BMUS, 5 SWE). An XGBoost MG-RadScore was derived via ten-fold cross-validation and integrated with clinical predictors in a final combined model. Performance was assessed by AUC, calibration, Hosmer-Lemeshow test, DCA, and SHAP.</p> Results <p>Diabetes duration (OR 1.13, 95% CI: 1.07–1.19, <i>P</i> &lt; 0.001) and right MG mean SWE modulus (RMG_SWEmean; OR 0.94, <i>P</i> = 0.022) were independent clinical predictors. The clinical model achieved AUCs of 0.741/0.780/0.783; the MG-RadScore model showed higher discrimination than it (0.833/0.788/0.804). The combined model achieved AUCs of 0.895 (95% CI: 0.839–0.940), 0.861 (95% CI: 0.745–0.949), and 0.854 (95% CI: 0.785–0.909). Calibration was satisfactory (Hosmer-Lemeshow <i>P</i> &gt; 0.05); DCA confirmed favorable net benefit; SHAP identified diabetes duration as the top predictor, followed by MG-RadScore and RMG_SWEmean.</p> Conclusion <p>A dual-modality MG ultrasound radiomics model integrating BMUS and SWE features with diabetes duration and RMG_SWEmean accurately detects DPN, achieving AUCs above 0.85 across multicenter prospective cohorts. This muscle-targeted, SHAP-interpretable approach offers a noninvasive alternative to nerve sonography and may facilitate early DPN screening where NCS is unavailable.</p>

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Dual-modality ultrasound radiomics of the medial gastrocnemius muscle for detecting diabetic peripheral neuropathy: a multicenter prospective study

  • 期刊论文

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  • 发表日期：

    2026年7月27日

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  • Wenqian Qiu,
  • Erfeng Chen,
  • Wanyan Li,
  • Rong Xiao,
  • Tingting Xie,
  • Danqing He

摘要

Background

Diabetic peripheral neuropathy (DPN) affects 20%–50% of patients with type 2 diabetes mellitus (T2DM), yet early screening is limited by restricted access to nerve conduction studies (NCS). We aimed to develop and validate a dual-modality ultrasound radiomics model combining B-mode ultrasound (BMUS) and shear wave elastography (SWE) of the medial gastrocnemius (MG) - an accessible surrogate reflecting neurogenic muscle atrophy - for noninvasive DPN detection.

Methods

A total of 190 T2DM patients (380 limbs) enrolled between June 2025 and February 2026 were allocated to training (158 limbs), internal test (68 limbs), and external validation (154 limbs) cohorts. DPN was defined as abnormal nerve conduction in at least two peripheral nerves, per ADA criteria. MG BMUS and SWE images were acquired bilaterally by blinded ultrasonographers. Sequential ICC (≥ 0.75), Mann-Whitney U (P < 0.05), Spearman (|r| > 0.7), and elastic-net filtering of 1,888 features selected 14 final features (9 BMUS, 5 SWE). An XGBoost MG-RadScore was derived via ten-fold cross-validation and integrated with clinical predictors in a final combined model. Performance was assessed by AUC, calibration, Hosmer-Lemeshow test, DCA, and SHAP.

Results

Diabetes duration (OR 1.13, 95% CI: 1.07–1.19, P < 0.001) and right MG mean SWE modulus (RMG_SWEmean; OR 0.94, P = 0.022) were independent clinical predictors. The clinical model achieved AUCs of 0.741/0.780/0.783; the MG-RadScore model showed higher discrimination than it (0.833/0.788/0.804). The combined model achieved AUCs of 0.895 (95% CI: 0.839–0.940), 0.861 (95% CI: 0.745–0.949), and 0.854 (95% CI: 0.785–0.909). Calibration was satisfactory (Hosmer-Lemeshow P > 0.05); DCA confirmed favorable net benefit; SHAP identified diabetes duration as the top predictor, followed by MG-RadScore and RMG_SWEmean.

Conclusion

A dual-modality MG ultrasound radiomics model integrating BMUS and SWE features with diabetes duration and RMG_SWEmean accurately detects DPN, achieving AUCs above 0.85 across multicenter prospective cohorts. This muscle-targeted, SHAP-interpretable approach offers a noninvasive alternative to nerve sonography and may facilitate early DPN screening where NCS is unavailable.