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Development and validation of MMR prediction model based on simplified clinicopathological features and serum tumour
Yinghao Cao1, Tao Peng2, Han Li3
1Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road No. 1277, Wuhan, Hubei 430022, China.
Background:
Although simplified clinicopathological features and serum tumour markers (STMs) were reported to be associated with the status of mismatch repair (MMR) in colorectal cancer (CRC) patients, their predictive value alone or in combination for MMR status remains unknown.
Methods:
A retrospective analysis of 3274 participants with MMR testing and STMs measurements from two institutions was conducted. The prediction model was developed in the primary cohort that consisted of 1964 participants. Best subset regression was applied to select the most useful predictors from the primary dataset. The performance of the nomogram was evaluated with respect to its calibration, discrimination, and clinical usefulness. External validation was performed in an independent validation cohort of 1310 consecutive CRC patients.
Findings:
Among the ten simplified clinicopathological features, seven variables were selected as the best subset of risk factors to develop pathology-based model, including age, tumour diameters, histology, tumour location, perineural invasion, the number of sampled lymph nodes (LNs) and positive LNs. The model showed good calibration and discrimination, with an AUC of 0.756 (95% CI, 0.722 to 0.789) in the primary cohort and 0.754 (95% CI, 0.715 to 0.793) in the validation cohort. After the addition of CEA and CA 72-4, the performance of pathology-based model was significantly improved in in both the primary cohort (AUC: 0.805 (0.774-0.835) vs. 0.756 (0.722-0.789), P < 0.001) and validation cohort (AUC: 0.796 (0.758-0.835) vs. 0.754 (0.715-0.793), P < 0.001). The results of decision curve analysis revealed that using our models to predict the status of MMR would add more benefit than either the detect-all-patients scheme or the detect-none scheme.
Interpretation:
The models based on simplified clinicopathological features alone or in combination with STMs can be conveniently used to facilitate the postoperative individualized prediction of MMR status in CRC patients.
