Correlation between diffusion kurtosis and intravoxel incoherent motion derived (IVIM) parameters and tumor tissue composition in rectal cancer: a pilot study

Yuan, Jie; Gong, Zhigang; Liu, Kun; Jin, Zhen; Wen, Qun; Tan, Weihan; Zhan, Siyan; Shen, Qiang

doi:10.1007/s00261-022-03426-3

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…Our study showed that MK was only weakly correlated with RSG and was not statistically significant in distinguishing between rich and poor stroma groups. The same conclusion was reached by Yuan et al and Mayer et al, 16,17 reporting on rectal and pancreatic cancer, respectively. This may indicate that RSG is not related to the complexity of the tumor tissue structure.…”

Section: Discussionsupporting

confidence: 78%

“…13 Recent studies have found a relationship between intratumor stromal fraction and IVIM and DKI parameters in breast, rectal, pancreatic, and soft tissue sarcomas. [14][15][16][17] For example, Fang et al 14 found a moderate correlation between ADC, D, and f values obtained from DWI and IVIM images with the quantitative histopathologic features in a murine model of rhabdomyosarcoma. Moreover, Li et al 15 found lower D and f values in low stromal content tumors than in high stromal patients with breast cancer.…”

mentioning

confidence: 99%

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Correlation of Intravoxel Incoherent Motion and Diffusion Kurtosis MR Imaging Models With Reactive Stromal Grade in Prostate Cancer

Duan

Fang

et al. 2022

Magnetic Resonance Imaging

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Background: Reactive stroma is recognized as one of the independent prognostic factors in prostate cancer (PCa). Intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) may be useful for assessing the reactive stromal grade (RSG). Purpose: To investigate whether IVIM and DKI models can evaluate RSG in PCa patients. Study Type: Retrospective. Subjects: A total of 56 PCa patients aged 73 years on average confirmed by MRI and transrectal ultrasound (MRI/TRUS) fusion biopsy divided into two subgroups (18 high RSG and 38 low RSG).

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Section: Discussionsupporting

confidence: 78%

mentioning

confidence: 99%

Correlation of Intravoxel Incoherent Motion and Diffusion Kurtosis MR Imaging Models With Reactive Stromal Grade in Prostate Cancer

Duan

Fang

et al. 2022

Magnetic Resonance Imaging

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“…Rectal cancer ranks among the predominant malignancies globally, witnessing a consistent rise in incidence over recent years [1][2][3], thus emerging as a signi cant contributor to cancer-related mortality [4]. The therapeutic modalities for rectal cancer encompass chemotherapy, radiotherapy, and surgical interventions, with the T stage serving as a critical determinant in the formulation of treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

Preoperative Prediction of Rectal Cancer Staging Combining MRI Deep Transfer Learning, Radiomics Features, and Clinical Factors: Accurate Differentiation from Stage T2 to T3

Fan,

Wu,

et al. 2024

Preprint

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Background This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer. Methods We included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data. Results After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the training group and 0.920 in the validation group. Conclusion The study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.

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Utilization of diffusion-weighted derived mathematical models to predict prognostic factors of resectable rectal cancer

Zhou,

Bao,

Huang

et al. 2024

Abdom Radiol

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Correlation between diffusion kurtosis and intravoxel incoherent motion derived (IVIM) parameters and tumor tissue composition in rectal cancer: a pilot study

Cited by 10 publications

References 26 publications

Correlation of Intravoxel Incoherent Motion and Diffusion Kurtosis MR Imaging Models With Reactive Stromal Grade in Prostate Cancer

Correlation of Intravoxel Incoherent Motion and Diffusion Kurtosis MR Imaging Models With Reactive Stromal Grade in Prostate Cancer

Preoperative Prediction of Rectal Cancer Staging Combining MRI Deep Transfer Learning, Radiomics Features, and Clinical Factors: Accurate Differentiation from Stage T2 to T3

Utilization of diffusion-weighted derived mathematical models to predict prognostic factors of resectable rectal cancer

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