Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models

Peterson, Kristin; Simpson, Matthew; Drezdzon, Melissa K.; Szabó, Anikó; Ausman, Robin A.; Nencka, Andrew S.; Knechtges, Paul; Peterson, Carrie Y.; Ludwig, Kirk; Ridolfi, Timothy J.

doi:10.1007/s11605-022-05477-9

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Currently, several studies have utilized inflammatory indexes or circulating lymphocyte subsets to predict treatment response in rectal cancers. 24 , 25 , 26 , 27 , 28 , 29 To our knowledge, so far, no study has reported the evaluation of the efficacy of NCRT for rectal MACs using the combination of inflammatory indexes and circulating lymphocyte subsets. In this study, we developed an XGB model based on preoperative clinicopathological parameters, inflammatory indexes, and the circulating lymphocyte subsets to predict treatment response to NCRT in rectal MAC patients.…”

Section: Discussionmentioning

confidence: 99%

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Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Lin,

Sun,

Jiang

et al. 2024

Cancer Medicine

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IntroductionIn this study, we aimed to evaluate the predictive value of circulating lymphocyte subsets and inflammatory indexes in response to neoadjuvant chemoradiotherapy (NCRT) in patients with rectal mucinous adenocarcinomas (MACs).MethodsRectal MAC patients who underwent NCRT and curative resection at Fujian Medical University Union Hospital's Department of Colorectal Surgery between 2016 and 2020 were included in the study. Patients were categorized into good and poor response groups based on their pathological response to NCRT. An independent risk factor‐based nomogram model was constructed by utilizing multivariate logistic regression analysis. Additionally, the extreme gradient boosting (XGB) algorithm was applied to build a machine learning (ML)‐based predictive model. Feature importance was quantified using the Shapley additive explanations method.ResultsOut of the 283 participants involved in this research, 190 (67.1%) experienced an unfavorable outcome. To identify the independent risk factors, logistic regression analysis was performed, considering variables such as tumor length, pretreatment clinical T stage, PNI, and Th/Tc ratio. Subsequently, a nomogram model was constructed, achieving a C‐index of 0.756. The ML model exhibited higher prediction accuracy than the nomogram model, achieving an AUROC of 0.824 in the training set and 0.762 in the tuning set. The top five important parameters of the ML model were identified as the Th/Tc ratio, neutrophil to lymphocyte, Th lymphocytes, Gross type, and T lymphocytes.ConclusionRadiochemotherapy sensitivity is markedly influenced by systemic inflammation and lymphocyte‐mediated immune responses in rectal MAC patients. Our ML model integrating clinical characteristics, circulating lymphocyte subsets, and inflammatory indexes is a potential assessment tool that can provide a reference for individualized treatment for rectal MAC patients.

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Section: Discussionmentioning

confidence: 99%

“…Currently, several studies have utilized inflammatory indexes or circulating lymphocyte subsets to predict treatment response in rectal cancers 24–29 . To our knowledge, so far, no study has reported the evaluation of the efficacy of NCRT for rectal MACs using the combination of inflammatory indexes and circulating lymphocyte subsets.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Lin,

Sun,

Jiang

et al. 2024

Cancer Medicine

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Optimizing and Validating Systemic DNA Damage Response Profiling to Predict Neoadjuvant Chemoradiation Response in Rectal Cancer

Demidova,

Czyzewicz,

Hasan

et al. 2024

Preprint

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Purpose. This study aimed to stratify patients with locally advanced rectal cancer (LARC) based on their response to neoadjuvant chemoradiation therapy (nCRT) using DNA damage response (DDR)-related proteins measured in peripheral blood monocytes (PBMCs). We optimized and validated an innovative assay to quantify these proteins, providing a predictive framework for nCRT response. Experimental Design. We used PBMCs collected from LARC patients either before or after standard course of ~5.5 weeks of nCRT, with patients categorized by neoadjuvant rectal (NAR) score. DDR was assessed by immunofluorescence (gammaH2AX foci), and by Luminex multi-analyte platform (xMAP) assay providing semi-quantitative assessment of phosphorylated Chk1, Chk2, gammaH2AX, p53 and total ATR, MDM2, p21. Assay performance was evaluated using reference controls and banked PBMCs from healthy controls (n=50). Results. PBMCs from poor responders (PoR; NAR >14; n=21) had significantly lower gammaH2AX foci than complete responders (CR; NAR <1; n=21) (p<0.0001), with no significant differences between pre- and post-nCRT samples (p=0.4961). The xMAP assay performance assessment showed linear sample curves, precision with acceptable inter- and intra-assay coefficients of variability, and high reproducibility with ~1% outliers in replicates. Clinical associations using the xMAP assay found levels of six proteins (ATR, MDM2, and phospho forms of Chk1, Chk2, gammaH2AX, and p53) significantly differentiating CRs from PoRs. Univariate CART analysis determined thresholds that segregated PoRs from CRs with high precision (p<0.001). Conclusion. We optimized an assay to assess DDR proteins in PBMCs and identified specific proteins, along with their threshold levels, that can accurately predict response to nCRT in patients with LARC.

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Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

Bourbonne

Schick

Pradier

et al. 2023

Cancers

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In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.

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Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models

Cited by 3 publications

References 37 publications

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Optimizing and Validating Systemic DNA Damage Response Profiling to Predict Neoadjuvant Chemoradiation Response in Rectal Cancer

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

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