A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation

doi:10.19401/j.cnki.1007-3639.2024.09.006

Abstract

Abstract:

Background and Purpose: The early prediction of local tumor progression-free survival (LTPFS) after radiofrequency ablation (RFA) for colorectal cancer (CRC) lung metastases has significant clinical importance. The application of radiomics in the prediction of tumor prognosis has been explored. This study aimed to construct a radiomics-based nomogram for predicting LTPFS after RFA in CRC patients with lung metastases. Methods: This study retrospectively analyzed 172 CRC patients with 401 lung metastases admitted to Department of Interventional Radiology, Fudan University Shanghai Cancer Center from August 2016 to January 2019. This study was reviewed by the medical ethics committee of Fudan University Shanghai Cancer Center (ethics number: 2402291-24). After augmentation of pre-ablation and immediate post-ablation computed tomography (CT) images, the target metastases and ablation regions were segmented manually to extract the radiomic features. Maximum relevance and minimum redundancy algorithm (MRMRA) and least absolute shrinkage and selection operator (LASSO) regression models were applied for feature selection. The clinical model, the radiomics model, and the fusion model were constructed based on the selected radiomic features and clinical variables screened by the multivariate analysis. The Harrell concordance index (C-index) and area under receiver operating characteristic (ROC) curves (AUC) were calculated to evaluate the prediction performance. Finally, the corresponding nomogram of the best model was drawn. Results: Among all the lung metastases, 102 (25.4%) had final recurrence, and 299 (74.6%) had complete response (CR). The median follow-up time was 21 months (95% CI: 19.466-22.534), and the LTPFS rates at 1, 2, and 3 years after RFA were 76.5% (95% CI: 72.0-80.4), 72.1% (95% CI: 66.6-76.9) and 69.9% (95% CI: 64.0-75.1). In both the training and test dataset, the fusion model based on the final 12 radiomic features through the LASSO regression and 4 clinical variables screened by multivariate analysis achieved the highest AUC values for LTPFS, with C-index values of 0.890 (95% CI: 0.854-0.927) and 0.843 (95% CI: 0.768-0.916), respectively. Conclusion: The fusion model based on radiomic features and clinical variables is feasible for predicting LTPFS after RFA of CRC patients with lung metastases, whose performance is superior to the single radiomic and clinical model. At the same time, the nomogram of the fusion model can intuitively predict the prognosis of CRC patients with lung metastases after RFA, thus assisting clinicians in developing individualized follow-up review plans for patients and adjusting treatment strategies flexibly.

Key words: Computed tomography, Radiomics, Nomogram, Colorectal cancer, Lung metastases, Radiofrequency ablation

CLC Number:

HUANG Haozhe, CHEN Hong, ZHENG Dezhong, CHEN Chao, WANG Ying, XU Lichao, WANG Yaohui, HE Xinhong, YANG Yuanyuan, LI Wentao. A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation[J]. China Oncology, 2024, 34(9): 857-872.

Figures/Tables 11

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References 33

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Characteristic	Training dataset (N=323)	Test dataset (N=78)	P value	Total (N=401)
Pre-RFA clinical characteristics
Gender n			0.947
Male	185	45		230
Female	138	33		171
Age/year M (Q1, Q3)	57 (50, 65)	61 (52, 66.25)	0.120
Initial tumor location n			0.166
Rectum	224	44		268
Sigmoid-left colon	40	15		55
Transverse-right colon	57	18		75
Caecum	2	1		3
Tumor biomarkers M (Q1, Q3)
CEA/(ng·mL^-1)	4.39 (2.07, 13.33)	3.96 (2.08, 11.38)	0.628
CA 19-9/(U·mL^-1)	10.71 (7.58, 19.50)	12.60 (6.83, 23.25)	0.928
Number of recurrence n			0.351
At 1 year	75	17		92
At 2 years	82	17		99
At 3 years	85	17		102
Follow-time/month M (95% CI)	25 (22.49, 27.51)	19 (18.43, 19.57)
Pre-RFA characteristics of lung metastases
Nodule size/mm n			0.064
<10	156	35		191
10-19	118	31		149
20-30	49	12		61
Location n			0.183
RUL	76	15		91
RML	36	8		44
RLL	63	10		73
LUL	77	18		95
LLL	71	27		98
Distance 1/cm n			0.454
≥1	266	67		333
<1	57	11		68
Distance 2/cm n			0.399
≥1	124	34		158
<1	199	44		243
Immediate post-RFA characteristics
Immediate pneumothorax n			0.851
Yes	82	19		101
No	241	59		300
Intra-alveolar hemorrhage n			0.778
Yes	86	22		108
No	237	56		293
Electrode chosen for RFA n			0.722
Expandable	303	74		377
Straight	20	4		24

Characteristic	Univariate analysis		Multivariate analysis
Characteristic	HR (95% CI)	P value	HR (95% CI)	P value
Clinical features
Gender
Male	1.055 (0.687-1.620)	0.806
Female	1
Age	1.003 (0.983-1.023)	0.748
Initial tumor location
Rectum	1
Sigmoid-left colon	0.663 (0.318-1.383)	0.273
Transverse-right colon	0.950 (0.532-1.695)	0.862
Caecum^*	-	-
Lymphadenopathy at diagnosis
Yes	1.034 (0.659-1.621)	0.884
No	1
Systemic therapy
Yes	0.817 (0.524-1.275)	0.374
No	1
Tumor biomarkers
CEA/(ng·mL^-1)	1.004 (1.001-1.007)	0.002	1.052 (1.021-1.084)	0.001
CA 19-9/(U·mL^-1)	1.003 (1.000-1.007)	0.062	1.047 (1.008-1.088)	0.019
Pre-RFA features of the lung metastases
Location
RUL	1.035 (0.506-2.112)	0.925	1.327 (0.660-2.668)	0.427
RML	1.612 (0.723-3.594)	0.243	1.645 (0.760-3.564)	0.207
RLL	2.588 (1.358-4.929)	0.004	3.055 (1.629-5.732)	0.001
LUL	1		1
LLL	1.732 (0.892-3.363)	0.104	2.236 (1.192-4.193)	0.012
Distance 1/cm
≥1	1
<1	1.326 (0.789-2.230)	0.287
Distance 2/cm
≥1	1
<1	1.206 (0.774-1.879)	0.409
Immediate post-RFA features
Pneumothorax
Yes	1.207 (0.758-1.923)	0.428
No	1
Hemorrhage
Yes	0.812 (0.492-1.339)	0.414
No	1
Electrode
Expandable	0.480 (0.240-0.958)	0.037	2.171 (1.176-4.005)	0.013
Straight	1		1

Pre-RFA radiomics feature	Post-RFA radiomics feature
Shape_Elongation	Shape_Sphericity
Shape_MeshVolume	GLCM_Idn^*
FirstOrder_Energy^*	GLCM_Idmn
FirstOrder_Entropy^*	GLDM_SmallDependenceLowGrayLevelEmphasis^*
FirstOrder_MeanAbsoluteDeviation^*	GLSZM_HighGrayLevelZoneEmphasis
FirstOrder_Skewness	NGTDM_Strength
FirstOrder_RootMeanSquared	NGTDM_Coarseness^*
FirstOrder_RobustMeanAbsoluteDeviation
GLCM_ClusterShade^*
GLCM_ClusterProminence
GLCM_Idmn
GLCM_Imc1^*
GLCM_InverseVariance^*
GLDM_DependenceEntropy^*
GLDM_GrayLevelNonUniformity^*
GLSZM_LargeAreaEmphasis
GLSZM_LargeAreaLowGrayLevelEmphasis^*

Variable	Fusion model	Radiomic model	Clinical model
Training dataset
C-index (95% CI)	0.890 (0.854-0.927)	0.867 (0.829-0.906)	0.665 (0.594-0.737)
Brier score (1-year)	0.092	0.106	0.161
Brier score (2-year)	0.160	0.189	0.180
Brier score (3-year)	0.161	0.198	0.177
AUC (1-year)	0.917	0.899	0.670
AUC (2-year)	0.869	0.850	0.668
AUC (3-year)	0.857	0.839	0.659
Test dataset
C-index (95% CI)	0.843 (0.768-0.916)	0.811 (0.722-0.897)	0.688 (0.565-0.811)
Brier score (1-year)	0.126	0.133	0.172
Brier score (2-year)	0.204	0.241	0.191
Brier score (3-year)	0.174	0.188	0.187
AUC (1-year)	0.857	0.810	0.711
AUC (2-year)	0.709	0.618	0.687
AUC (3-year)	0.757	0.674	0.655