Artificial intelligence recognition of pathological T stage and tumor invasion in rectal cancer based on large panoramic pathological sections

Ju, Yiheng; Zheng, Longbo; Zhao, Peng; Xin, Fangjie; Wang, Fengjiao; Gao, Yuan; Zhang, Xianxiang; Wang, Dongsheng; Lu, Yi

doi:10.1016/j.imed.2022.03.004

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In recent years, the rapid development of emerging radiomics technologies has been accelerated by improvements in big data processing technologies, such as artificial intelligence. 7,8 Through high-throughput quantitative analysis and the application of automated data characterization algorithms to conduct in-depth mining of medical image information, deep quantitative features that cannot be recognized by the naked eye in medical images are extracted, which has become a bridge between imaging and precision medicine. For example, there have been widespread reports that MRI radiomics features may be used as potential imaging markers for the early prediction of locally advanced rectal cancer (LARC) response to neoadjuvant therapy.…”

Section: Introductionmentioning

confidence: 99%

Development of a joint prediction model based on both the radiomics and clinical factors for preoperative prediction of circumferential resection margin in middle‐low rectal cancer using T2WI images

Ju,

Zheng,

et al. 2023

Medical Physics

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ObjectivesA circumferential resection margin (CRM) is an independent risk factor for local recurrence, distant metastasis, and poor overall survival of rectal cancer. In this study, we developed and validated a radiomics prediction model to predict perioperative surgical margins in patients with middle and low rectal cancer following neoadjuvant treatment and for decisions about treatment plans for patients.MethodsThis study retrospectively analyzed 275 patients from center 1(training cohort) and 120 patients from center 2(verification cohort) with rectal cancer diagnosed at two centers from July 2020 to July 2022 who underwent neoadjuvant therapy and had their CRM status confirmed by preoperative high‐resolution magnetic resonance imaging (MRI) scans. Radiomics signatures were extracted and screened from MRI images and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model, which was combined with clinical signatures to construct a nomogram. The receiver operating characteristic (ROC) curve and area under the curve (AUC) value, sensitivity, specificity, positive predictive value, negative predictive value, and calibration curve were used to evaluate the predictive performance of the model.ResultsIn our research, the combined model has the best performance. In the training group, the radiomics model based on high‐spatial‐resolution T2‐weighted imaging (HR‐T2WI), clinical model and combined model demonstrated an AUC of 0.819 (0.802–0.833), 0.843 (0.822–0.861), and 0.910 (0.880‐0.940), respectively. In the validation group, they demonstrated an AUC of 0.745 (0.715–0.788), 0.827 (0.798–0.850), and 0.848 (0.779‐0.917), respectively. The calibration curve confirmed the clinical applicability of the model.ConclusionsThe individualized prediction model established by combining radiomics signatures and clinical signatures can efficiently and objectively predict perioperative margin invasion in patients with middle and low rectal cancer.

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Section: Introductionmentioning

confidence: 99%