Rationale and Objectives
To develop and validate a computed tomography (CT)-based radiomics nomogram for the
preoperative prediction of tumor deposits (TDs) and clinical outcomes in patients
with colon cancer.
Materials and Methods
This retrospective study included 383 consecutive patients with colon cancer from
two centers. Radiomics features were extracted from portal venous phase CT images.
Least absolute shrinkage and selection operator regression was applied for feature
selection and radiomics signature construction. The multivariate logistic regression
model was used to establish a radiomics nomogram. The performance of the nomogram
was assessed by using receiver operating characteristic curves, calibration curves
and decision curve analysis. Kaplan‒Meier survival analysis was used to assess the
difference of the overall survival (OS) in the TDs-positive and TDs-negative groups.
Results
The radiomics signature was composed of 11 TDs status related features. The AUCs of
the radiomics model in the training cohort, internal validation and external validation
cohorts were 0.82, 0.78 and 0.78, respectively. The radiomics nomogram that incorporated
the radiomics signature and clinical independent predictors (CT-N, CEA and CA199)
showed good calibration and discrimination with AUCs of 0.88, 0.80 and 0.81 in the
training cohort, internal validation and external validation cohorts, respectively.
The radiomics nomogram-predicted high-risk groups had a worse OS than the low-risk
groups (p < 0.001). The radiomics nomogram-predicted TDs was an independent preoperative predictor
of OS.
Conclusion
The radiomics nomogram based on CT radiomics features and clinical independent predictors
could effectively predict the preoperative TDs status and OS of colon cancer.
Important Findings
CT-based radiomics nomogram may be applied in the individual preoperative prediction
of TDs status in colon cancer. Additionally, there was a significant difference in
OS between the high-risk and low-risk groups defined by the radiomics nomogram, in
which patients with high-risk TDs had a significantly worse OS, compared with those
with low-risk TDs.
Key Words
Abbreviations:
TDs (tumor deposits), CRC (colorectal cancer), CT (computed tomography), CEA (carcinoembryonic antigen), CA199 (carbohydrate antigen 199), LDH (lactate dehydrogenase), A/G (albumin to globulin ratio), CT-T (CT-reported T stage), CT-N (CT-reported lymph node status), ROI (regions of interest), VOI (volume of interest), 2D (Two-dimensional), 3D (Three-dimensional), ICC (interclass correlation coefficient), ROC (receiver operating characteristic), AUC (area under the ROC curve), DCA (decision curve analysis), LASSO (Least absolute shrinkage and selection operator), mRMR (Maximal redundancy minimal relevance), GLCM (gray level cooccurrence matrix), OS (overall survival)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2021; 71: 209-249https://doi.org/10.3322/caac.21660
- Cancer statistics, 2019.CA Cancer J Clin. 2019; 69: 7-34https://doi.org/10.3322/caac.21551
- Colorectal tumour deposits in the mesorectum and pericolon; a critical review.Histopathology. 2007; 51: 141-149https://doi.org/10.1111/j.1365-2559.2007.02720.x
- Tumor deposits in colorectal cancer: a moving target.Ann Surg. 2012; 255: 214-215https://doi.org/10.1097/SLA.0b013e3182430eaa
- Significance of extranodal tumour deposits in colorectal cancer: A systematic review and meta-analysis.Eur J Cancer. 2017; 82: 92-102https://doi.org/10.1016/j.ejca.2017.05.027
- Tumor deposits in stage III colon cancer: correlation with other histopathologic variables, prognostic value, and risk stratification-time to consider "N2c".Am J Clin Oncol. 2020; 43: 133-138https://doi.org/10.1097/COC.0000000000000645
- Peritumoral deposits as an adverse prognostic indicator of colorectal cancer.Am J Surg. 2014; 207: 70-77https://doi.org/10.1016/j.amjsurg.2013.04.009
- Tumor deposits in colorectal cancer: improving the value of modern staging-a systematic review and meta-analysis.J Clin Oncol. 2017; 35: 1119-1127https://doi.org/10.1200/JCO.2016.68.9091
- Prognostic value of tumor deposits for long-term oncologic outcomes in patients with stage III colorectal cancer: a systematic review and meta-analysis.Int J Colorectal Dis. 2022; 37: 141-151https://doi.org/10.1007/s00384-021-04036-z
- The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging.CA Cancer J Clin. 2017; 67: 93-99https://doi.org/10.3322/caac.21388
- Mesorectal microfoci adversely affect the prognosis of patients with rectal cancer.Dis Colon Rectum. 2002; 45: 733-743https://doi.org/10.1007/s10350-004-6288-8
- Neoplastic mesorectal microfoci (MMF) following neoadjuvant chemoradiotherapy: clinical and prognostic implications.Ann Surg Oncol. 2007; 14: 853-861https://doi.org/10.1245/s10434-006-9163-0
- Pathological changes of advanced lower-rectal cancer by preoperative radiotherapy.Hepatogastroenterology. 2004; 51: 1362-1366
- Prognostic significance of MR identified EMVI, tumour deposits, mesorectal nodes and pelvic side wall disease in locally advanced rectal cancer.Colorectal Dis. 2022; 24: 428-438https://doi.org/10.1111/codi.16032
- MRI-diagnosed tumor deposits and EMVI status have superior prognostic accuracy to current clinical TNM staging in rectal cancer.Ann Surg. 2022; 276: 334-344https://doi.org/10.1097/SLA.0000000000004499
- Can extranodal tumour deposits be diagnosed on MRI? Protocol for a multicentre clinical trial (the COMET trial).BMJ Open. 2020; 10 (Published 2020 Oct 7)e033395https://doi.org/10.1136/bmjopen-2019-033395
- Tumor deposits: markers of poor prognosis in patients with locally advanced rectal cancer following neoadjuvant chemoradiotherapy.Oncotarget. 2016; 7: 6335-6344https://doi.org/10.18632/oncotarget.6656
- The role of tumor deposits in predicting the efficacy of chemotherapy in stage III colon cancer.Front Oncol. 2020; 10 (Published 2020 Oct 14)586603https://doi.org/10.3389/fonc.2020.586603
- Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?.Eur Radiol. 2018; 28: 2058-2067https://doi.org/10.1007/s00330-017-5146-8
- Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer.J Clin Oncol. 2016; 34: 2157-2164https://doi.org/10.1200/JCO.2015.65.9128
- Computed tomography-based radiomics for predicting lymphovascular invasion in rectal cancer.Eur J Radiol. 2022; 146110065https://doi.org/10.1016/j.ejrad.2021.110065
- The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer.Abdom Radiol (NY). 2022; 47: 1244-1254https://doi.org/10.1007/s00261-022-03453-0
- Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer.Eur Radiol. 2022; 32: 714-724https://doi.org/10.1007/s00330-021-08167-3
- A CT-based radiomics nomogram in predicting the postoperative prognosis of colorectal cancer: a two-center study.Acad Radiol. 2022; (S1076-6332(22)00117-9)https://doi.org/10.1016/j.acra.2022.02.006
- Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study.Eur Radiol. 2022; 32: 405-414https://doi.org/10.1007/s00330-021-08104-4
- Computed tomography-based radiomics for preoperative prediction of tumor deposits in rectal cancer.Front Oncol. 2021; 11 (Published 2021 Sep 27)710248https://doi.org/10.3389/fonc.2021.710248
- Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.Eur Radiol. 2020; 30: 1969-1979https://doi.org/10.1007/s00330-019-06558-1
- High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer.Abdom Radiol (NY). 2021; 46: 873-884https://doi.org/10.1007/s00261-020-02733-x
- Colon cancer CT staging according to mismatch repair status: comparison and suggestion of imaging features for high-risk colon cancer.Eur J Cancer. 2022; 174: 165-175https://doi.org/10.1016/j.ejca.2022.06.060
- Impact of visceral adipose tissue on the accuracy of T-staging by CT in colon cancer.Eur J Radiol. 2021; 134109400https://doi.org/10.1016/j.ejrad.2020.109400
- Assessment and diagnostic accuracy of lymph node status to predict stage III colon cancer using computed tomography.Cancer Imaging. 2017; 17: 3https://doi.org/10.1186/s40644-016-0104-2
- Morphological predictors for lymph node metastases on computed tomography in colon cancer.Abdom Radiol (NY). 2019; 44: 1712e21
- Development and validation of a radiomics model for differentiating bone islands and osteoblastic bone metastases at abdominal CT.Radiology. 2021; 299: 626-632https://doi.org/10.1148/radiol.2021203783
- A review of feature selection methods in medical applications.Comput Biol Med. 2019; 112103375https://doi.org/10.1016/j.compbiomed.2019.103375
- Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram.Br J Radiol. 2022; 9520211014https://doi.org/10.1259/bjr.20211014
- Comparison of clinical-computed tomography model with 2D and 3D radiomics models to predict occult peritoneal metastases in advanced gastric cancer.Abdom Radiol (NY). 2022; 47: 66-75https://doi.org/10.1007/s00261-021-03287-2
- Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma.Br J Radiol. 2022; 9520210918https://doi.org/10.1259/bjr.20210918
- Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features.Med Phys. 2018; 45: 5116-5128https://doi.org/10.1002/mp.13202
- High pretreatment serum CA19-9 level predicts a poor prognosis for patients with stage III colon cancer after curative resection and adjuvant chemotherapy.J Cancer. 2019; 10 (Published 2019 Jun 9): 3810-3818https://doi.org/10.7150/jca.31375
- Effect of tumor deposits on overall survival in colorectal cancer patients with regional lymph node metastases.J Rural Med. 2014; 9: 20-26https://doi.org/10.2185/jrm.2880
- Tumor deposit is an independent prognostic indicator in patients who underwent radical resection for colorectal cancer.J Cancer. 2018; 9 (Published 2018 Oct 10): 3979-3985https://doi.org/10.7150/jca.27475
Article info
Publication history
Published online: December 23, 2022
Accepted:
November 7,
2022
Received in revised form:
October 16,
2022
Received:
July 26,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
