The potential benefit of adjuvant chemotherapy in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy is not predicted by tumor regression grade

2019 ◽  
Vol 45 (2) ◽  
pp. e113
Author(s):  
A. Bohlok ◽  
A. Hendlisz ◽  
B. Fikri ◽  
M. Gomezgaldon ◽  
J. Vandestadt ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Adjuvant Chemotherapy ◽  
Potential Benefit ◽  
Tumor Regression ◽  
Locally Advanced ◽  
Neoadjuvant Chemoradiotherapy ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer ◽  
Tumor Regression Grade ◽  
Regression Grade

Modified 3-Point MRI-Based Tumor Regression Grade Incorporating DWI for Locally Advanced Rectal Cancer

2017 ◽  
Vol 209 (6) ◽  
pp. 1247-1255 ◽  
Author(s):  
Min A Lee ◽  
Seung Hyun Cho ◽  
An Na Seo ◽  
Hye Jung Kim ◽  
Kyung-Min Shin ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Tumor Regression ◽  
Locally Advanced ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer ◽  
Tumor Regression Grade ◽  
Regression Grade

scholarly journals Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1894 ◽  
Author(s):  
Bianca Petresc ◽  
Andrei Lebovici ◽  
Cosmin Caraiani ◽  
Diana Sorina Feier ◽  
Florin Graur ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Tumor Regression ◽  
Locally Advanced ◽  
Neoadjuvant Treatment ◽  
Neoadjuvant Chemoradiotherapy ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer ◽  
Imaging Biomarkers ◽  
Tumor Regression Grade ◽  
Pre Treatment

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

scholarly journals Utility of ctDNA in predicting response to neoadjuvant chemoradiotherapy and prognosis assessment in locally advanced rectal cancer: A prospective cohort study

PLoS Medicine ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. e1003741
Author(s):  
Yaqi Wang ◽  
Lifeng Yang ◽  
Hua Bao ◽  
Xiaojun Fan ◽  
Fan Xia ◽  
...  
Keyword(s):  
Tumor Regression ◽  
Locally Advanced ◽  
Neoadjuvant Chemoradiotherapy ◽  
Complete Response ◽  
Advanced Rectal Cancer ◽  
Recurrence Risk ◽  
Locally Advanced Rectal Cancer ◽  
Tumor Regression Grade ◽  
Plasma Samples ◽  
Regression Grade

Background For locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (nCRT), there are no reliable indicators to accurately predict pathological complete response (pCR) before surgery. For patients with clinical complete response (cCR), a “Watch and Wait” (W&W) approach can be adopted to improve quality of life. However, W&W approach may increase the recurrence risk in patients who are judged to be cCR but have minimal residual disease (MRD). Magnetic resonance imaging (MRI) is a major tool to evaluate response to nCRT; however, its ability to predict pCR needs to be improved. In this prospective cohort study, we explored the value of circulating tumor DNA (ctDNA) in combination with MRI in the prediction of pCR before surgery and investigated the utility of ctDNA in risk stratification and prognostic prediction for patients undergoing nCRT and total mesorectal excision (TME). Methods and findings We recruited 119 Chinese LARC patients (cT3-4/N0-2/M0; median age of 57; 85 males) who were treated with nCRT plus TME at Fudan University Shanghai Cancer Center (China) from February 7, 2016 to October 31, 2017. Plasma samples at baseline, during nCRT, and after surgery were collected. A total of 531 plasma samples were collected and subjected to deep targeted panel sequencing of 422 cancer-related genes. The association among ctDNA status, treatment response, and prognosis was analyzed. The performance of ctDNA alone, MRI alone, and combining ctDNA with MRI was evaluated for their ability to predict pCR/non-pCR. Ranging from complete tumor regression (pathological tumor regression grade 0; pTRG0) to poor regression (pTRG3), the ctDNA clearance rate during nCRT showed a significant decreasing trend (95.7%, 77.8%, 71.1%, and 66.7% in pTRG 0, 1, 2, and 3 groups, respectively, P = 0.008), while the detection rate of acquired mutations in ctDNA showed an increasing trend (3.8%, 8.3%, 19.2%, and 23.1% in pTRG 0, 1, 2, and 3 groups, respectively, P = 0.02). Univariable logistic regression showed that ctDNA clearance was associated with a low probability of non-pCR (odds ratio = 0.11, 95% confidence interval [95% CI] = 0.01 to 0.6, P = 0.04). A risk score predictive model, which incorporated both ctDNA (i.e., features of baseline ctDNA, ctDNA clearance, and acquired mutation status) and MRI tumor regression grade (mrTRG), was developed and demonstrated improved performance in predicting pCR/non-pCR (area under the curve [AUC] = 0.886, 95% CI = 0.810 to 0.962) compared with models derived from only ctDNA (AUC = 0.818, 95% CI = 0.725 to 0.912) or only mrTRG (AUC = 0.729, 95% CI = 0.641 to 0.816). The detection of potential colorectal cancer (CRC) driver genes in ctDNA after nCRT indicated a significantly worse recurrence-free survival (RFS) (hazard ratio [HR] = 9.29, 95% CI = 3.74 to 23.10, P < 0.001). Patients with detectable driver mutations and positive high-risk feature (HR_feature) after surgery had the highest recurrence risk (HR = 90.29, 95% CI = 17.01 to 479.26, P < 0.001). Limitations include relatively small sample size, lack of independent external validation, no serial ctDNA testing after surgery, and a relatively short follow-up period. Conclusions The model combining ctDNA and MRI improved the predictive performance compared with the models derived from individual information, and combining ctDNA with HR_feature can stratify patients with a high risk of recurrence. Therefore, ctDNA can supplement MRI to better predict nCRT response, and it could potentially help patient selection for nonoperative management and guide the treatment strategy for those with different recurrence risks.

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