Dataset Information

Nomograms for predicting pathological response to neoadjuvant treatments in patients with rectal cancer.

ABSTRACT: BACKGROUND:In recent decades, neoadjuvant therapy (NT) has been the standardized treatment for locally advanced rectal cancer (LARC). Approximately 8%-35% of patients with LARC who received NT were reported to have achieved a complete pathological response (pCR). If the pathological response (PR) can be accurately predicted, these patients may not need surgery. In addition, no response after NT implies that the tumor is destructive, resistant to both chemotherapy and radiotherapy, and prone to having a high metastatic potential. Therefore, developing accurate models to predict PR has great clinical significance and can help achieve individualized treatment in LARC patients. AIM:To establish nomograms for predicting PR to different NT regimens based on pretreatment parameters for patients with LARC. METHODS:Rectal cancer patients were identified from the database of The Sixth Affiliated Hospital, Sun Yat-sen University from January 2012 to December 2016. Logistic regression and nomograms were developed to predict the probability of pCR and good downstaging to ypT0-2N0M0 (ypTNM 0-I), respectively, based on pretreatment parameters for all LARC patients. Nomograms were also developed for three NT regimens (capecitabine/deGramont-RT, mFOLFOX6, and mFOLFOX6-RT) to predict pCR probability. RESULTS:Four hundred and three patients were included in this study; 72 (17.9%) had pCR at the final pathology report, and 177 (43.9%) achieved good downstaging to ypT0-2N0M0 (ypTNM 0-I). The nomogram for predicting pCR probability showed that NT regimens, tumor differentiation, mesorectal fascia (MRF) status, and tumor length significantly influenced pCR probability. When predicting the probability of good downstaging, tumor differentiation, MRF status, and clinical T stage were the significant factors. Nomograms were developed based on NT regimens. For the capecitabine/de Gramont-RT group, the multivariate analysis showed that the neutrophil-lymphocyte ratio (NLR) was the only significant factor, thus we could not develop a nomogram for this regimen. For the mFOLFOX6-RT group, the analysis showed that the significant factors were tumor length and MRF status; and for the mFOLFOX6 group, the significant factors were tumor length and tumor differentiation. CONCLUSION:We established accurate nomograms for predicting the PR to preoperative NT regimens based on pretreatment parameters for LARC patients.

SUBMITTER: Ren DL

PROVIDER: S-EPMC6328965 | biostudies-other | 2019 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Nomograms for predicting pathological response to neoadjuvant treatments in patients with rectal cancer.

Ren Dong-Lin DL Li Juan J Yu Hui-Chuan HC Peng Shao-Yong SY Lin Wei-Da WD Wang Xiao-Lin XL Ghoorun Roshan Ara RA Luo Yan-Xin YX

World journal of gastroenterology 20190101 1

<h4>Background</h4>In recent decades, neoadjuvant therapy (NT) has been the standardized treatment for locally advanced rectal cancer (LARC). Approximately 8%-35% of patients with LARC who received NT were reported to have achieved a complete pathological response (pCR). If the pathological response (PR) can be accurately predicted, these patients may not need surgery. In addition, no response after NT implies that the tumor is destructive, resistant to both chemotherapy and radiotherapy, and pr ...[more]

PMID: 30643363

Similar Datasets

Project description:BackgroundWe aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature.MethodsThis was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score.ResultsWe developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990-1.000] and 0.822 [95% CI 0.649-0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did.ConclusionsThe CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.

Project description:Different neoadjuvant therapy regimens are available for rectal cancer, but the relative effects are controversial. The aim of the present network meta-analysis (NMA) was to estimate the relative efficacy and safety of neoadjuvant therapies for resectable rectal cancer. MEDLINE, EMBASE and Cochrane Central Registry of Controlled Trials were searched for publications dated from 1946 up to June 2018. The present study included randomized clinical trials that compared treatments for resected rectal cancer: Surgery alone, surgery preceded by neoadjuvant radiotherapy (RT), neoadjuvant chemotherapy (CT) or neoadjuvant chemoradiotherapy (CRT). Direct pairwise comparisons and NMA were conducted. A total of 23 randomized controlled trials were included in the present study. RT had an overall survival (OS) benefit when compared with surgery alone [HR (hazard ratio), 0.89; 95% confidence interval (CI), 0.82-0.97; quality of evidence, high]. All three neoadjuvant regimens were associated with lower local recurrence (LR) when compared with surgery alone [RT: odds ratio (OR), 0.44; 95% CI, 0.35-0.65; quality of evidence, high; CRT: OR, 0.34; 95% CI, 0.23-0.56; quality of evidence, low and CT: OR, 0.32; 95% CI, 0.11-1.00; quality of evidence, low]. There were no significant differences in OS and LR between CRT and RT (OS: OR, 1.10); 95% CI, 0.93-1.20; LR: OR, 0.81; 95% CI, 0.61-1.10). Ranking probabilities indicated that CRT was the best strategy for local control, with a surface under the cumulative ranking curve (SUCRA) of 78.78%. Patients treated with RT had improved disease-free survival compared with those treated with surgery alone (HR, 0.82; 95% CI, 0.64-1.00; quality of evidence, low). Neoadjuvant RT or CRT did not significantly improve distant metastases compared with surgery alone (RT: OR, 0.87; 95% CI, 0.69-1.10 and CRT: OR, 0.75; 95% CI, 0.47-1.10). CRT had an improved pathological complete response rate compared with RT (OR, 4.90; 95% CI, 21.80-17.00; quality of evidence, low). No significant difference for the risk of anastomotic leak between each treatment was observed in the NMA. In conclusion, RT decreased the LR and improved OS compared with surgery alone for resected rectal cancer. CRT was the best neoadjuvant therapy analyzed and CT was likely the second best for all outcomes based on SUCRA. However, these findings were limited by overall low quality of evidence.

Dataset Information

Nomograms for predicting pathological response to neoadjuvant treatments in patients with rectal cancer.

Publications

Nomograms for predicting pathological response to neoadjuvant treatments in patients with rectal cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets