The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.

The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.

Objective To investigate the imaging characteristics and diagnostic value of tri-phase dynamic enhancement scan with CT for acute renal infarction. Methods The image features of CT plain scan and tri-phase dynamic enhancement scan of 10 patients (19 sides) with acute renal infarction were retrospectively analyzed, and the CTA expression of 6 patients were observed. Results Fourteen acute renal infarction lesions of 10 cases were diagnosed. The CT scan showed there were 4 cases with enlargement of kidney, and the other 6 cases were of no abnormality. The tri-phase enhancement CT scan showed there were 6 cases of unilateral renal infarction and 4 cases of bilateral renal infarction, which totally involving 14 sides. The acute renal infarction lesions lacked of high density region in the corticomedullary in cortical phase, and there were wedge-shaped hypodense area, even low density of full kidney in parenchymal phase and pyelographic phase. The a-cute renal infarction lesions were revealed better in parenchymal phase and pyelographic phase than in cortical phase. Six cases of CTA re-vealed the responsible vessels of renal infarction lesions and the other vascular diseases. Conclusion CT tri-phase dynamic enhancement scan has important value in the diagnosis of acute renal infarction, and CTA can identify the responsible vessels of renal infarction lesions.