Additional file 1 of An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer
Creators: name: Pan, Jiexin
name: Hong, Guibin
name: Zeng, Hong
name: Liao, Chengxiao
name: Li, Huarun
name: Yao, Yuhui
name: Gan, Qinghua
name: Wang, Yun
name: Wu, Shaoxu
name: Lin, Tianxin
Publisher: name: figshare
Year: 2023
Subjects: subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 1: Table S1. WSI-level diagnostic performance of the PAIDM in validation set 2. Table 2. Diagnostic accuracy of the PAIDM and pathologists in validation set 3. Figure S1. Examples of six labels for fully annotated images. (a) HGMI contained both high-grade tumour cells and bladder muscle tissue. (b) HGNMI only contained high-grade tumour cells. (c) LGNMI only contained low-grade tumour cells. (d) IA was defined as the blurred area due to scanning. (e) NIA included all normal mesenchymal cells. (f) NA indicated the noncellular area due to staining. HGMI=high-grade muscle invasion. HGNMI=high-grade non-muscle invasion. LGNMI=low-grade non-muscle invasion. IA=illegible area. NIA=normal interstitial area. NA=noise area. Figure S2. Image preprocessing and patch sampling. (a) The OTSU algorithm was used to eliminate the white backgrounds to improve the efficiency of diagnosis and analysis. The red boxes are the preserved tissue area, and the white backgrounds were removed. (b) The sliding window method was used to extract patches of the HGNMI, LGNMI, IA, NIA and NA types. The sampling points were within the annotation region to guarantee that each patch comprised just the certain kind of tissue. (c) The point-based labelling method was adopted to extract the typical patches of HGMI. The sampling points were marked at the junction of tumour tissue and muscle tissue. The green points are the sampling points of the HGMI type. HGMI=high-grade muscle invasion. HGNMI=high-grade non-muscle invasion. LGNMI=low-grade non-muscle invasion. IA=illegible area. NIA=normal interstitial area. NA=noise area. Figure S3. Learning curves of the PAIDM. The learning curves of the PAIDM show the gradual decrease of loss (a) and increase of accuracy (b) during the training process. PAIDM=pathological artificial intelligence diagnostic model. Figure S4. The proportion of patients with HGMI, HGNMI and LGNMI subtypes in training set and three validation sets. HG= high grade. LG= low grade. NMIBC=non-muscle-invasive bladder cancer. MIBC= muscle-invasive bladder cancer. Figure S5. ROC curves for WSI-level diagnostic performance of the PAIDM in validation set 1. PAIDM=pathological artificial intelligence diagnostic model. WSI= whole slide image. ROC=receiver operating characteristic. AUC=area under the curve. LGNMI=low-grade non-muscle invasion. HGNMI=high-grade non-muscle invasion. HGMI=high-grade muscle invasion. Figure S6. Confusion matrices of the PAIDM in the two validation sets. (a) Confusion matrix for the patch-level classification in validation set 1. (b) Confusion matrix for the WSI-level classification in validation set 1. (c) Confusion matrix for the WSI-level classification in validation set 2. PAIDM=pathological artificial intelligence diagnostic model. WSI= whole slide image. LGNMI=low-grade non-muscle invasion. HGNMI=high-grade non-muscle invasion. HGMI=high-grade muscle invasion. Figure S7. Diagnostic parameters to assess the PAIDM at the WSI level in validation set 2. AUC=area under the curve. CI=confidence interval. PPV=positive predictive value. NPV=negative predictive value. HG= high grade. LG= low grade. NMIBC=non-muscle-invasive bladder cancer. MIBC= muscle-invasive bladder cancer. PAIDM=pathological artificial intelligence diagnostic model. WSI=whole slide image. Figure S8. Diagnostic performance of the PAIDM in six-class recognition at the patch level in validation set 1. (a) ROC curves for patch-level diagnostic performance of six classes. (b) Confusion matrix for patch-level diagnostic performance of six classes. PAIDM=pathological artificial intelligence diagnostic model. ROC=receiver operating characteristic. AUC=area under the curve. HGMI=high-grade muscle invasion. HGNMI=high-grade non-muscle invasion. LGNMI=low-grade non-muscle invasion. IA=illegible area. NIA=normal interstitial area. NA=noise area.