Dataset Information

Response to neoadjuvant treatment among rectal cancer patients in a population-based cohort.

ABSTRACT:

Background

In rectal cancer, prediction of tumor response and pathological complete response (pCR) to neoadjuvant treatment could contribute to refine selection of patients who might benefit from a delayed- or no-surgery approach. The aim of this study was to explore the association of clinical and molecular characteristics of rectal cancer with response to neoadjuvant treatment and to compare patient survival according to level of response.

Methods

Resected rectal cancer patients were selected from a population-based cohort study. Molecular tumor markers were determined from the surgical specimen. Tumor response and pCR were defined as downstaging in T or N stage and absence of tumor cells upon pathological examination, respectively. The associations of patient and tumor characteristics with tumor response and pCR were explored, and patient survival was determined by degree of response to neoadjuvant treatment.

Results

Among 1536 patients with rectal cancer, 602 (39%) received neoadjuvant treatment. Fifty-five (9%) patients presented pCR, and 239 (49%) and 250 (53%) patients showed downstaging of the T and N stages, respectively. No statistically significant associations were observed between patient or tumor characteristics and tumor response or pCR. Patients who presented any type of response to neoadjuvant treatment had significantly better cancer-specific and overall survival compared with non-responders.

Conclusion

In this study, patient characteristics were not associated with response to neoadjuvant treatment, and molecular characteristics determined after surgical resection of the tumor were not predictive of pCR or tumor downstaging. Future studies should include molecular biomarkers from biopsy samples before neoadjuvant treatment.

SUBMITTER: Alwers E

PROVIDER: S-EPMC7782441 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Response to neoadjuvant treatment among rectal cancer patients in a population-based cohort.

Alwers Elizabeth E Jansen Lina L Kather Jakob J Amitay Efrat E Bläker Hendrik H Kloor Matthias M Tagscherer Katrin E KE Roth Wilfried W Herpel Esther E Chang-Claude Jenny J Brenner Hermann H Hoffmeister Michael M

International journal of colorectal disease 20200919 1

<h4>Background</h4>In rectal cancer, prediction of tumor response and pathological complete response (pCR) to neoadjuvant treatment could contribute to refine selection of patients who might benefit from a delayed- or no-surgery approach. The aim of this study was to explore the association of clinical and molecular characteristics of rectal cancer with response to neoadjuvant treatment and to compare patient survival according to level of response.<h4>Methods</h4>Resected rectal cancer patients ...[more]

PMID: 32949260

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Project description:Background and Aims: Although the wait and watch (W&W) strategy is a treatment choice for locally advanced rectal cancer (LARC) patients who achieve clinical complete response (cCR) after neoadjuvant therapy (NT), the issue on consistency between cCR and pathological CR (pCR) remains unsettled. Herein, we aimed to develop a deep convolutional neural network (DCNN) model using endoscopic images of LARC patients after NT to distinguish tumor regression grade (TRG) 0 from non-TRG0, thus providing strength in identifying surgery candidates. Methods: A total of 1000 LARC patients (6,939 endoscopic images) who underwent radical surgery after NT from April 2013 to April 2021 at the Sixth Affiliated Hospital, Sun Yat-sen University were retrospectively included in our study. Patients were divided into three cohorts in chronological order: the training set for constructing the model, the validation set, and the independent test set for validating its predictive capability. Besides, we compared the model's performance with that of three endoscopists on a class-balanced, randomly selected subset of 20 patients' LARC images (10 TRG0 patients with 70 images and 10 non-TRG0 patients with 72 images). The measures used to evaluate the efficacy for identifying TRG0 included overall accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). Results: There were 219 (21.9%) cases of TRG0 in the included patients. The constructed DCNN model in the training set obtained an excellent performance with good accuracy of 94.21%, specificity of 94.39%, NPV of 98.11%, and AUROC of 0.94. The validation set showed accuracy, specificity, NPV, and AUROC of 92.13%, 93.04%, 96.69%, and 0.95, respectively; the corresponding values in the independent set were 87.14%, 92.98%, 91.37%, and 0.77, respectively. In the reader study, the model outperformed the three experienced endoscopists with an AUROC of 0.85. Conclusions: The proposed DCNN model achieved high specificity and NPV in detecting TRG0 LARC tumors after NT, with a better performance than experienced endoscopists. As a supplement to radiological images, this model may serve as a useful tool for identifying surgery candidates in LARC patients after NT.

Project description:Background: Patients with rectal cancer who achieve pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) may have a better prognosis and may be eligible for non-operative management. The aim of this research was to identify variables for predicting pCR in rectal cancer patients after nCRT and to define clinical risk factors for poor outcome after pCR to nCRT and radical resection in rectal cancer patients. Methods: A retrospective review was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2013. Non-metastatic rectal cancer patients who received radical resection after neoadjuvant chemoradiotherapy were included in this study. Multivariate analysis of the association between clinicopathological characteristics and pCR was performed, and a logistic regression model was used to identify independent predictors for pCR. A nomogram based on the multivariate logistics regression was built with decision curve analyses to evaluate the clinical usefulness. Results: A total of 6,555 patients were included in this study. The proportion of patients with pCR was 20.5% (n = 1,342). The nomogram based on multivariate logistic regression analysis showed that clinical T4 and N2 stages were the most significant independent clinical predictors for not achieving pCR, followed by mucinous adenocarcinoma and positive pre-treatment serum CEA results. The 3-year overall survival rate was 92.4% for those with pCR and 88.2% for those without pCR. Among all the pCR patients, mucinous adenocarcinoma patients had the worst survival, with a 3-year overall survival rate of 67.5%, whereas patients with common adenocarcinoma had an overall survival rate of 93.8% (P < 0.001). Univariate and multivariate analyses showed that histology and clinical N2 stage were independent risk factors. Conclusion: Mucinous adenocarcinoma, positive pre-treatment serum CEA results, and clinical T4 and N2 stages may impart difficulty for patients to achieve pCR. Mucinous adenocarcinoma and clinical N2 stage might be indicative of a prognostically unfavorable biological tumor profile with a greater propensity for local or distant recurrence and decreased survival.

Project description: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.

Project description:BACKGROUND:Neoadjuvant radiotherapy (RT) has been shown to improve local control; however, whether it can improve overall survival (OS) in locally advanced rectal cancer (LARC) patients remains controversial. We therefore aimed to examine the benefits of surgery alone, neoadjuvant radiotherapy (RT), adjuvant RT, and surgery plus chemotherapy in stage II (T3/4N0M0) and III (any T and N?+?M0) on the OS of rectal cancer patients. METHODS:Date from the Surveillance, Epidemiology, and End Results (SEER) database diagnosed between 2004 and 2016 were used. Kaplan-Meier analyses were used to compare patient prognoses across different treatment modalities. Cox hazard regression analysis were used to identify independent predictors of OS. RESULTS:For stage T3/4N0M0 patients, neoadjuvant RT, adjuvant RT, and surgery plus chemotherapy resulted in similar OS (all p?>?0.05; mean survival, 115.89?months (M), 111.97?M, and 117.22?M, respectively), with better OS observed in these patients than in patients who underwent surgery alone (all p?<?0.001, mean survival, 88.96?M). For stage T1/2N?+?M0 patients, neoadjuvant RT, adjuvant RT, and surgery plus chemotherapy resulted in similar OS (all p?>?0.05; mean survival, 121.50?M, 124.25?M, and 121.20?M, respectively), with better OS observed in these patients than in patients who underwent surgery alone (all p?<?0.001, mean survival 83.81?M). For stage T3/4N?+?M0 patients, neoadjuvant RT (HR?=?0.436; 95% CI, 0.396~0.478; p?<?0.001) resulted in significantly longer OS than adjuvant RT and surgery plus chemotherapy (mean survival, 104.47?M, 93.94?M, and 93.62?M, respectively), with better OS observed in these patients than in patients who underwent surgery alone (all p?<?0.001, mean survival 54.87?M). Older age (>?60?years), black race, unmarried status, high tumour grade, and tumour size >?5?cm were all associated with a poor prognosis (all p?<?0.05). CONCLUSIONS:Neoadjuvant RT, adjuvant RT, and surgery plus chemotherapy results in better OS than surgery alone in LARC patients. Neoadjuvant RT has the potential to be highly recommended over adjuvant RT and surgery plus chemotherapy for T3/4N?+?M0 patients; however, it showed no OS advantage over adjuvant RT or surgery plus chemotherapy for T3/4N0M0 and T1/2N?+?M0 patients.

Dataset Information

Response to neoadjuvant treatment among rectal cancer patients in a population-based cohort.

Background

Methods

Results

Conclusion

Publications

Response to neoadjuvant treatment among rectal cancer patients in a population-based cohort.

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