MRI-based radiomic score increased mrTRG accuracy in predicting rectal cancer response to neoadjuvant therapy

Miranda, Joao; Horvat, Natally; Assunção, Antonildes Nascimento; Machado, Felipe Augusto de M; Chakraborty, Jayasree; Pandini, Rafael Vaz; Saraiva, Samya; Nahas, Caio Sérgio Rizkallah; Nahas, Sérgio Carlos; Nomura, César Higa

doi:10.1007/s00261-023-03898-x

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…Preoperative neoadjuvant chemoradiotherapy was an important part of rectal cancer treatment ( 28 ), and many previous studies had reported prediction models for the response of rectal cancer to preoperative neoadjuvant therapy ( 18 , 29 , 30 ). However, cases of progression after preoperative neoadjuvant therapy could not be ignored.…”

Section: Disscussionmentioning

confidence: 99%

“…The prediction of disease progression after preoperative neoadjuvant therapy and operative treatment is of great significance. Moreover, previous studies had reported the prediction of the effect of preoperative neoadjuvant, such as MRI ( 18 ), circulating DNA ( 19 ), tumor microsatellite stability ( 20 ), immune cell infiltration ( 21 ), etc. However, these studies only considered a few variables, and the true magnitude of the effect needed to be clarified.…”

Section: Introductionmentioning

confidence: 99%

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Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Hu,

Sheng,

et al. 2024

Front. Oncol.

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BackgroundWe attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.MethodsData from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.ResultsEventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.ConclusionsWe constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.

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Section: Disscussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Hu,

Sheng,

et al. 2024

Front. Oncol.

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“…The total mrTRG score has been associated with disease-free and overall survival ( 30 ) , as well as having shown moderate accuracy for detecting a cPR ( 31 ) . Specifically, a post-NAT mrTRG score of 1 or 2 (indicating complete or substantial radiological regression, respectively) has been shown to have a sensitivity of 70–71% and a specificity of 62–68% for a cPR ( 32 , 33 ) . Although the use of the mrTRG system has shown some benefits, it is crucial to acknowledge the limited correlation between the mrTRG and pathologic TRG scores.…”

Section: Restaging Rectal Mrimentioning

confidence: 99%

Restaging magnetic resonance imaging of the rectum after neoadjuvant therapy: a practical guide

Horvat,

Miranda,

Kinochita

et al. 2024

Radiol Bras

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Colorectal cancer is the third most common cancer and the second leading cause of cancer-related death. Rectal cancer accounts for approximately one-third of new colorectal cancer cases, with adenocarcinoma as the predominant subtype. Despite an overall decline in colorectal cancer incidence and mortality, due to advancements in screening, early diagnosis, and treatment options, there is a concerning increase in incidence rates among young patients. Recent significant advances in managing locally advanced rectal cancer, such as the establishment of different surgical approaches, neoadjuvant treatment using different protocols for high-risk cases, and the adoption of organ-preservation strategies, have increased the importance of the role played by radiologists in locoregional assessment on magnetic resonance imaging at baseline, at restaging, and during active surveillance of patients with rectal cancer. In this article, we review the role of restaging rectal magnetic resonance imaging after neoadjuvant therapy, providing radiologists with a practical, step-by-step guide for assessing treatment response.

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“…However, it is important to note that there is a limited correlation between mrTRG and pathologic TRG. A meta-analysis by Jang et al found that ymrTRG scores of 1–2, which correspond to complete or good radiological regression, had a sensitivity of 70–71% and a specificity of 62–68% [ 58 , 59 ]. Moreover, the inter-reader agreement of ymrTRG is highly variable, with kappa values ranging from 0.25 to 0.8 [ 60 , 61 , 62 ].…”

Section: Rectal Mri Response Assessmentmentioning

confidence: 99%

Advances in MRI-Based Assessment of Rectal Cancer Post-Neoadjuvant Therapy: A Comprehensive Review

Miranda,

Causa Andrieu,

Nincevic

et al. 2023

JCM

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Rectal cancer presents significant diagnostic and therapeutic challenges, with neoadjuvant therapy playing a pivotal role in improving resectability and patient outcomes. MRI serves as a critical tool in assessing treatment response. However, differentiating viable tumor tissue from therapy-induced changes on MRI remains a complex task. In this comprehensive review, we explore treatment options for rectal cancer based on resectability status, focusing on the role of MRI in guiding therapeutic decisions. We delve into the nuances of MRI-based evaluation of treatment response following neoadjuvant therapy, paying particular attention to emerging techniques like radiomics. Drawing from our insights based on the literature, we provide essential recommendations for post-neoadjuvant therapy management of rectal cancer, all within the context of MRI-based findings.

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MRI-based radiomic score increased mrTRG accuracy in predicting rectal cancer response to neoadjuvant therapy

Cited by 5 publications

References 36 publications

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Restaging magnetic resonance imaging of the rectum after neoadjuvant therapy: a practical guide

Advances in MRI-Based Assessment of Rectal Cancer Post-Neoadjuvant Therapy: A Comprehensive Review

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