J Cancer 2021; 12(15):4561-4573. doi:10.7150/jca.58887

Research Paper

Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer

Tengfei Li1,2#, Zekuan Yu3,4✉#, Yan Yang2#, Zhongmao Fu1#, Ziang Chen5, Qi Li6, Kundong Zhang1, Zai Luo1, Zhengjun Qiu1, Chen Huang1✉

1. Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 201600, China.
2. Graduate School of Bengbu Medical College, Bengbu 233000, China.
3. Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
4. Key Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education.
5. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
6. Department of Medical Oncology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200021, China.
#These authors contributed equally to the work.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Li T, Yu Z, Yang Y, Fu Z, Chen Z, Li Q, Zhang K, Luo Z, Qiu Z, Huang C. Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer. J Cancer 2021; 12(15):4561-4573. doi:10.7150/jca.58887. Available from https://www.jcancer.org/v12p4561.htm

File import instruction


Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis.

Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software.

Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual.

Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making.

Keywords: Colorectal cancer, Tumor stroma percentage, Rapid multi-dynamic, Threshold algorithm, Gary image