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The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer

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Abstract

Purpose

Perineural invasion (PNI) has been recognized as an important prognosis factor in patients with colorectal cancer (CRC). The purpose of this retrospective study was to investigate the value of 18F-FDG PET/CT-based radiomics integrating clinical information, PET/CT features, and metabolic parameters for preoperatively predicting PNI and outcome in non-metastatic CRC and establish an easy-to-use nomogram.

Methods

A total of 131 patients with non-metastatic CRC who undergo PET/CT scan were retrospectively enrolled. Univariate analysis was used to compare the differences between PNI-present and PNI-absent groups. Multivariate logistic regression was performed to select the independent predictors for PNI status. Akaike information criterion (AIC) was used to select the best prediction models for PNI status. CT radiomics signatures (RSs) and PET-RSs were selected by maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) regression and radiomics scores (Rad-scores) were calculated for each patient. The prediction models with or without Rad-score were established. According to the nomogram, nomogram scores (Nomo-scores) were calculated for each patient. The performance of different models was assessed with the area under the curve (AUC), specificity, and sensitivity. The clinical usefulness was assessed by decision curve (DCA). Multivariate Cox regression was used to selected independent predictors of progression-free survival (PFS).

Results

Among all the clinical information, PET/CT features, and metabolic parameters, CEA, lymph node metastatic on PET/CT (N stage), and total lesion glycolysis (TLG) were independent predictors for PNI (p < 0.05). Six CT-RSs and 12 PET-RSs were selected as the most valuable factors to predict PNI. The Rad-score calculated with these RSs was significantly different between PNI-present and PNI-absent groups (p < 0.001). The AUC of the constructed model was 0.90 (95%CI: 0.83–0.97) in the training cohort and 0.80 (95%CI: 0.65–0.95) in the test cohort. The nomogram’s predicting sensitivity was 0.84 and the specificity was 0.83 in the training cohort. The clinical model’s predicting sensitivity and specificity were 0.66 and 0.85 in the training cohort, respectively. Besides, DCA showed that patients with non-metastatic CRC could get more benefit with our model. The results also indicated that N stage, PNI status, and the Nomo-score were independent predictors of PFS in patients with non-metastatic CRC.

Conclusion

The nomogram, integrating clinical data, PET/CT features, metabolic parameters, and radiomics, performs well in predicting PNI status and is associated with the outcome in patients with non-metastatic CRC.

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Data availability

The datasets generated and analyzed during the current study are not publicly available due to patient privacy concerns, but they are available from the corresponding author upon reasonable request.

Abbreviations

FDG:

Fluorodeoxyglucose

PET/CT:

Positron emission tomography/computed tomography

PNI:

Perineural invasion

CRC:

Colorectal cancer

AIC:

Akaike information criterion

RSs:

Radiomics signatures

Rad-score:

Radiomics score

Nomo-score:

Nomogram score

AUC:

Area under curve

DCA:

Decision curve analysis

PFS:

Progression-free survival

CEA:

Carcinoembryonic antigen

TLG:

Total lesion glycolysis

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Authors and Affiliations

  1. Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59 Hair Road, Qingdao, Shandong, China

    Jie Ma, Wenjie Miao, Yangyang Wang, Lei Yan, Fengyu Wu, Guangjie Yang & Zhenguang Wang

  2. Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Dong Guo

  3. Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Chuantao Zhang

  4. Huiying Medical Technology Co.Ltd, Beijing, China

    Ran Zhang & Panli Zuo

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  1. Jie Ma
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Ma, J., Guo, D., Miao, W. et al. The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer. Abdom Radiol 47, 1244–1254 (2022). https://doi.org/10.1007/s00261-022-03453-0

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