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Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer?

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Abstract

Purpose

To determine whether rectal filling with ultrasound gel is clinically more beneficial in preoperative T staging of patients with rectal cancer (RC) using radiomics model based on magnetic resonance imaging (MRI).

Methods

A total of 94 RC patients were assigned to cohort 1 (leave-one-out cross-validation [LOO-CV] set) and 230 RC patients were assigned to cohort 2 (test set). Patients were grouped according to different pathological T stages. The radiomics features were extracted through high-resolution T2-weighted imaging for all volume of interests in the two cohorts. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. Model 1 (without rectal filling) and model 2 (with rectal filling) were constructed. LOO-CV was adopted for radiomics model building in cohort 1. Thereafter, the cohort 2 was used to test and verify the effectiveness of the two models.

Results

Totally, 204 patients were enrolled, including 60 cases in cohort 1 and 144 cases in cohort 2. Finally, seven optimal features with LASSO were selected to build model 1 and nine optimal features were used for model 2. The ROC curves showed an AUC of 0.806 and 0.946 for model 1 and model 2 in cohort 1, respectively, and an AUC of 0.783 and 0.920 for model 1 and model 2 in cohort 2, respectively (p = 0.021).

Conclusion

The radiomics model with rectal filling showed an advantage for differentiating T1 + 2 from T3 and had less inaccurate categories in the test cohort, suggesting that this model may be useful for T-stage evaluation.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

N/A.

Abbreviations

RC:

Rectal cancer

TME:

Total mesorectal excision

ICC:

Intraclass correlation coefficient

T2WI:

T2-weighted imaging

VOI:

Volume of interest

LASSO:

Least absolute shrinkage and selection operator

XGBoost:

Extreme gradient boosting

LOO-CV:

Leave-one-out cross-validation

ROC:

Receiver operating characteristic

AUC:

Area under the ROC curve

MRF:

Mesorectal fascia

EMVI:

Extramural vascular invasion

LN:

Lymph node

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Funding

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Author information

Author notes

  1. Yuan Yuan, Haidi Lu, and Xiaolu Ma contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China

    Yuan Yuan, Haidi Lu, Xiaolu Ma, Fangying Chen, Shaoting Zhang, Minjie Wang, Chengwei Shao, Jianping Lu & Fu Shen

  2. Huiying Medical Technology Co., Ltd, B2, Dongsheng Science and Technology Park, HaiDian District, Beijing, China

    Yuwei Xia

Authors
  1. Yuan Yuan
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  2. Haidi Lu
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Contributions

YY performed segmentation, analyzed, and is a major contributor in writing the manuscript. HL performed segmentation, analyzed, and is a major contributor in writing the manuscript. XM acquired the data and interpreted the patient data regarding radiomics features. FC interpreted the patient data regarding radiomics features. SZ analyzed and interpreted the patient data regarding radiomics features. MW acquired the data. YX performed statistical analysis. FS conceived the project. JL conceived the project. CS conceived the project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fu Shen.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Consent to participate

Informed consent was waived for this retrospective study.

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Ethical approval

All methods of the present research were approved by the local Institutional Review Board of Changhai Hospital.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplemental

Table 1 Oblique axial High-resolution T2WI sequence parameters (DOCX 21 KB)

Supplemental

Table 2 ROC analysis of XGBoost models and subjective assessment for both cohorts (DOCX 18 KB)

Supplemental

Table 3 Comparison of proposed models in cohort 1 (DOCX 20 KB)

Supplemental Table 4

Logistic regression analyses of associations between potential predictors and pathological T stage in cohort 1 (DOCX 20 KB)

Supplemental

Fig. 1. The repeatability of radiomics features using ICCs on different scanners. (TIF 2879 KB)

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Yuan, Y., Lu, H., Ma, X. et al. Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer?. Abdom Radiol 47, 1741–1749 (2022). https://doi.org/10.1007/s00261-022-03477-6

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  • DOI: https://doi.org/10.1007/s00261-022-03477-6

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