ObjectiveTo explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters and apparent diffusion coefficient (ADC) value in evaluating the differentiation degrees and T stages of rectal cancer.MethodsThe patients with rectal cancer from November 2017 to November 2019 in the Sichuan Provincial People’s Hospital were collected. The volume transfer constant (Ktrans), flux rate constant (Kep), and extravascular extracellular volume fraction (Ve), and ADC values of the tumors were measured and compared in the patients with the different differentiation degrees and T stages. The receiver operating characteristic (ROC) curve analysis was performed.ResultsAll of 53 eligible patients were included, including 13 cases of high differentiation, 30 cases of medium differentiation, and 10 cases of low differentiation; 5 cases of T1 stage, 8 cases of T2 stage, 24 cases of T3 stage, and 16 cases of T4 stage. ① There were statistical differences in the Ktrans and ADC values among the different differentiation degrees of rectal cancer (P=0.004, P<0.001), and no statistical differences in the Kep and Ve values (P>0.050) among them. The Ktrans value was increased with decreased differentiation degree (P<0.050), the ADC value was decreased with decreased differentiation degrees (P<0.050). ② There were statistical differences in the Ktrans and ADC values among the different T stages of rectal cancer (P=0.002; P=0.007), and no statistical differences in the Kep and Ve values (P>0.050) among them. The Ktrans and ADC values were statistically different between the T2 and T3 stages of rectal cancer (P=0.009, P=0.013). ③ The Ktrans and ADC values could distinguish the high and medium differentiation degrees of rectal cancer, its area under ROC curve (AUC) values were 0.677 and 0.763, respectively, and the corresponding best thresholds were 0.180/min and 1.179 mm2/s; The Ktrans and ADC values could distinguish the medium and low differentiation degrees of rectal cancer, its AUC values were 0.693 and 0.967, and the corresponding best thresholds were 0.281/min and 0.906 mm2/s; The Ktrans and ADC values could distinguish the T2 and T3 stages of rectal cancer, its AUC values were 0.862 and 0.742, and the corresponding best thresholds were 0.204/min and 1.579 mm2/s.ConclusionDCE-MRI quantitative parameters and ADC value before surgery to determine the different differentiation degrees and T stages of rectal cancer have certain reference value, the best Ktrans and ADC thresholds to distinguish different differentiation degrees and T2 to T3 stages can be obtained through statistical analysis.
ObjectiveTo evaluate the value of imaging quantification parameters in artificial intelligence (AI) assisted diagnosis systems in clinical decision-making for lung nodules ≤2 cm and the diagnostic efficacy of AI. MethodsLung nodule patients admitted to Zhongshan Hospital affiliated with Dalian University from 2020 to 2023 were included. Imaging parameters of lung nodules were extracted using AI assisted diagnosis systems. Multifactor analysis was used to screen predictors for distinguishing benign and malignant nodules and high-risk predictors for recurrent invasive adenocarcinoma, and a diagnostic model was established and its performance evaluated. The diagnostic efficacy of the AI system was judged according to pathological results. ResultsA total of 594 patients with lung nodules were included, including 202 males and 392 females, with an average age of 24-82 (58.75±11.55) years. Volume, average CT value, and 3D maximum diameter of non-solid nodules were independent predictors of malignant nodules, with thresholds of 287.4 mm3, -491 HU, and 12.0 mm, respectively. The area under the curve (AUC) for diagnostic efficacy was ranked from high to low as combined model (0.802), volume (0.783), average CT value (0.749), and 3D maximum diameter (0.714); the average CT value and 3D long diameter of solid nodules were independent predictors of malignant nodules, with thresholds of -81 HU and 17.5 mm, respectively, and AUC values of 0.874 and 0.686, respectively, with the combined prediction AUC of 0.957; the mass of cystic nodules was an independent predictor of malignancy when the mass >180.7 mg. Independent predictors of high recurrence risk of invasive adenocarcinoma in non-solid nodules were consolidation-tumor ratio (CTR), average CT value, 3D long diameter, and volume, with thresholds of 0.14, -386 HU, 15.6 mm, and 1018.9 mm3, respectively, and diagnostic efficacy was ranked from high to low as combined model (0.788), 3D long diameter (0.735), volume (0.725), average CT value (0.720), and CTR (0.697). The accuracy of AI in predicting benign and malignant target nodules was 87.4%, with positive predictive value of 96.6% and negative predictive value of 58.9%. ConclusionIn clinical decision-making for lung nodules ≤2 cm, AI assisted diagnosis systems have high application value.