Wei Jiang1,2, Min Li3, Jie Tan1, Mingyuan Feng1, Jixiang Zheng1, Dexin Chen1, Zhangyuanzhu Liu1, Botao Yan1, Guangxing Wang4, Shuoyu Xu1,5, Weiwei Xiao3, Yuanhong Gao6, Shuangmu Zhuo7, Jun Yan8. 1. Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China. 2. School of Science, Jimei University, Xiamen, Fujian, People's Republic of China. 3. Department of Radiation Oncology, Sun Yat sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China. 4. Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, Fujian, People's Republic of China. 5. Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China. 6. Department of Radiation Oncology, Sun Yat sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China. gaoyh@sysucc.org.cn. 7. School of Science, Jimei University, Xiamen, Fujian, People's Republic of China. shuangmuzhuo@gmail.com. 8. Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China. yanjunfudan@163.com.
Abstract
BACKGROUND: The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients. METHODS: In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort. RESULTS: The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT. CONCLUSIONS: The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.
BACKGROUND: The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients. METHODS: In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort. RESULTS: The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT. CONCLUSIONS: The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.
Authors: Monique Maas; Regina G H Beets-Tan; Doenja M J Lambregts; Guido Lammering; Patty J Nelemans; Sanne M E Engelen; Ronald M van Dam; Rob L H Jansen; Meindert Sosef; Jeroen W A Leijtens; Karel W E Hulsewé; Jeroen Buijsen; Geerard L Beets Journal: J Clin Oncol Date: 2011-11-07 Impact factor: 44.544
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Authors: A J Breugom; W van Gijn; E W Muller; Å Berglund; C B M van den Broek; T Fokstuen; H Gelderblom; E Kapiteijn; J W H Leer; C A M Marijnen; H Martijn; E Meershoek-Klein Kranenbarg; I D Nagtegaal; L Påhlman; C J A Punt; H Putter; A G H Roodvoets; H J T Rutten; W H Steup; B Glimelius; C J H van de Velde Journal: Ann Oncol Date: 2014-12-05 Impact factor: 32.976