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Session: ePoster: Colon and Colorectal Cancer 13 (Mon1015)

EP708 - Transanal multipoint full-layer puncture biopsy for predicting rectal cancer patients response to neoadjuvant therapy

Monday, May 12, 2025
10:53 AM – 10:58 AM PT

    Author(s)

  • YJ

    Ying Jin, n/a

    Dr
    Beijing Chaoyang Hospital, Capital Medical University
    Beijing, Beijing, China (People's Republic)

    Disclosure information not submitted.

  • YD

    Yong Dai, n/a

    Dean
    Department of General Surgery, Qilu Hospital of Shandong University
    Jinan, Shandong, China (People's Republic)

    Disclosure information not submitted.

  • HY

    Hong Wei Yao, n/a

    Dean
    Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing
    Beijing, Beijing, China (People's Republic)

    Disclosure information not submitted.

  • QQ

    Qun Qian, n/a

    Dean
    Department of General Surgery, Zhongnan Hospital of Wuhan University
    Wuhan, Hubei, China (People's Republic)

    Disclosure information not submitted.

    • ePoster Presenter(s)

  • Jiagang Han, MD

    Dean
    Gerneral Surgery, Beijing Chaoyang Hospital, Capital Medical University
    Beijing, Beijing, China (People's Republic)

    All of the relevant financial relationships listed below have been mitigated.
    This individual has no financial relationships with ineligible companies.

    • Senior Author(s)

  • ZW

    Zhenjun Wang, n/a

    Dean
    Gerneral Surgery, Beijing Chaoyang Hospital, Capital Medical University
    Beijing, Beijing, China (People's Republic)

    All of the relevant financial relationships listed below have been mitigated.
    This individual has no financial relationships with ineligible companies.

All of the relevant financial relationships listed below have been mitigated.
Jiagang Han, MD: This individual has no financial relationships with ineligible companies.
Zhenjun Wang, n/a: This individual has no financial relationships with ineligible companies.
Image or Table (Do Not add text here):

Purpose/Background: Patients who achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) generally have a favorable prognosis. Accurately assessing response following nCRT may allow some patients to safely forgo surgery. Based on experience with multiple prostate biopsies, we conducted 16F core needle biopsies at a minimum of 13 locations surrounding the tumor bed or scar tissue under direct visualization to enhance pCR prediction accuracy following nCRT for LARC patients. Preliminary results indicated that transanal multipoint full-layer puncture biopsy (TMFP) significantly improved the accuracy of cCR from 74.4% to 92.6%. However, current models for predicting pCR have limited accuracy, highlighting the need for new assessment methods to enhance predictive capabilities.

Methods/Interventions: In this multicenter prospective study, we included patients with locally advanced rectal cancer (LARC) who underwent total mesorectal excision (TME) following nCRT between April 2020 and June 2023. All patients received transanal multipoint full-layer puncture (TMFP) before TME. We considered 23 factors, including TMFP, to select the model with the highest area under the curve (AUC) and applied the SHapley Additive Explanations (SHAP) method to evaluate the effects of individual characteristics on model output.

Results/Outcomes: A total of 129 LARC patients were eligible for participation, including 79 patients with non-pCR and 50 with pCR. TMFP demonstrated a sensitivity of 100%, specificity of 83.5%, positive predictive value of 79.4%, negative predictive value of 100%, and accuracy of 89.9% in predicting pCR, with no serious complications reported. TMFP showed significantly higher predictive accuracy than clinical complete response (cCR) (89.9% vs. 66.7%, χ2 = 20.527, P < 0.001). Among the ten constructed models, the adaptive boosting (AdaBoost) model performed best, achieving an AUC of 0.924 and accuracy of 0.884. Feature importance analysis revealed that the top five influential features were TMFP, distance from the anal verge, cCR, pre-chemoradiotherapy CEA levels and pre-chemoradiotherapy CA199 levels, with TMFP being the most influential. SHAP force analysis illustrated how the model can visualize personalized predictions for pCR.

Conclusion/Discussion: Our findings suggest that machine learning methods incorporating TMFP significantly enhance the prediction of pCR following nCRT in LARC patients. The proposed modified-cCR concept, which combines TMFP and cCR, could improve the accuracy of cCR in estimating pCR and support the safety of future watch-and-wait (W&W) studies.