AIM: To evaluate the clinical parameters and identify a better method of predicting pathological complete response (pCR). METHODS: We enrolled 249 patients from a database of 544 consecutive rectal cancer patients who underwent surgical resection after preoperative chemoradiation therapy (PCRT). A retrospective review of morphological characteristics was then performed to collect data regarding rectal examination findings. A scoring model to predict pCR was then created. To validate the ability of the scoring model to predict complete regression.RESULTS: Seventy patients (12.9%) achieved a pCR. A multivariate analysis found that pre-CRT movability (P = 0.024), post-CRT size (P = 0.018), post-CRT morphology (P = 0.023), and gross change (P = 0.009) were independent predictors of pCR. The accuracy of the scoring model was 76.8% for predicting pCR with the threshold set at 4.5. In the validation set, the accuracy was 86.7%. CONCLUSION: Gross changes and morphological findings are important predictors of pathological response. Accordingly, PCRT response is best predicted by a combination of clinical, laboratory and metabolic information. METHODS: We enrolled 249 patients from a database of 544 consecutive rectal cancer patients who underwent surgical resection after preoperative chemoradiation therapy (PCRT). A retrospective review of morphological characteristics was then performed to collect data regarding rectal examination findings. A validated the ability of the scoring model to predict complete regression .RESULTS: Seventy patients (12.9%) achieved a pCR. A multivariate analysis found that pre-CRT movability (P = 0.024), post-CRT size (P = 0.018), post-CRT morphology accuracy of the scoring model was 76.8% for predicting pCR with the threshold set at 4.5. In the validation set, the accuracy was 86.7%. CONCLUSION: Gross changes and morphological findings are importan t predictors of pathological response., PCRT response is best predicted by a combination of clinical, laboratory and metabolic information.