Application of artificial intelligence-driven three-dimensional imaging in preoperative planning for rectosigmoid colon cancer

Figure 1 Clinical cohort establishment flowchart. DTAV: Distance from the tumor’s lowest border to the anal verge; AI-3D: Artificial intelligence-driven digital three-dimensional imaging; ICC: Intraclass correlation coefficients; CT: Computed tomography.

Figure 2 Three techniques for detecting tumor burden and the distance from the tumor’s lowest border to the anal verge. A: Detect tumor burden in colorectal cancer using computed tomography equipment; B: Detect distance from the tumor’s lowest border to the anal verge (DTAV) using colonoscopy equipment; C: Detect tumor burden in colorectal cancer and DTAV using artificial intelligence-driven digital three-dimensional. DTAV: Distance from the tumor’s lowest border to the anal verge; AI-3D: Artificial intelligence-driven digital three-dimensional imaging; CT: Computed tomography.

Figure 3 The flowchart of artificial intelligence-driven digital three-dimensional imaging. A: Computed tomography-based automatic segmentation and three-dimensional reconstruction; B: Automated screening and segmentation of enlarged lymph nodes; C: Tumor-to-anal-verge distance estimation. CT: Computed tomography.

Figure 4 Artificial intelligence-driven digital three-dimensional imaging digital imaging render. A: Cross-sectional imaging view of the tumor; B: Sagittal imaging view of the tumor; C: Location of the rectal tumor (yellow area); D: Tumor size (a: 3.39 cm, b: 3.03 cm); E: Distance from the tumor’s lowest border to the anal verge measurement result (a: 15.63 cm); F: Enlarged lymph nodes.

Figure 5 Bland-Altman and scatter plots evaluating the pairwise agreement in measurements of the maximum tumor diameter among the three methods. A: Scatter plots of agreement between measurements from pathological specimens and artificial intelligence-driven digital three-dimensional imaging (AI-3D digital imaging); B: Bland-Altman of agreement between measurements from pathological specimens and AI-3D digital imaging; C: Scatter plots of agreement between measurements from pathological specimens and computed tomography (CT); D: Bland-Altman of agreement between measurements from pathological specimens and CT; E: Scatter plots of agreement between measurements from AI-3D digital imaging and CT; F: Bland-Altman of agreement between measurements from AI-3D digital imaging and CT. AI-3D: Artificial intelligence-driven digital three-dimensional imaging; CT: Computed tomography.

Figure 6 Bland-Altman and scatter plots evaluating the pairwise agreement in measurements of the maximum tumor cross-sectional area among the three methods. A: Scatter plots of agreement between measurements from pathological specimens and artificial intelligence-driven digital three-dimensional imaging (AI-3D digital imaging); B: Bland-Altman of agreement between measurements from pathological specimens and AI-3D digital imaging; C: Scatter plots of agreement between measurements from pathological specimens and computed tomography (CT); D: Bland-Altman of agreement between measurements from pathological specimens and CT; E: Scatter plots of agreement between measurements from AI-3D digital imaging and CT; F: Bland-Altman of agreement between measurements from AI-3D digital imaging and CT. AI-3D: Artificial intelligence-driven digital three-dimensional imaging; CT: Computed tomography.

Figure 7 Evaluation of the distance from the tumor's lowest border to the anal verge and lymph node involvement using artificial intelligence-driven digital three-dimensional imaging compared with colonoscopy and computed tomography. A and B: Bland-Altman plot and Scatter plot of agreement between measurements from artificial intelligence-driven digital three-dimensional imaging (AI-3D digital imaging) and colonoscopy; C: Intraclass correlation coefficient cluster diagram (1: The maximum tumor diameter; 2: The maximum tumor cross-sectional area; 3: Distance from the tumor’s lowest border to the anal verge); D: Predictive evaluation of lymph node involvement using AI-3D and computed tomography (1: Sensitivity; 2: Specificity; 3: Positive predictive value; 4: Negative predictive value); E: Receiver operating characteristic curves of the two techniques. AI-3D: Artificial intelligence-driven digital three-dimensional imaging; CT: Computed tomography.