A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Figures & data

Figure 1 Recruitment procedure and study flowchart.

Table 1 Characteristics of Patients in the Training and Validation Cohorts

Characteristics	Training Cohort			Validation Cohort
	OLN (+)	OLN (-)	P	OLN (+)	OLN (-)	P
Age, mean ± SD (y)	59.94 ± 8.30	62.56 ± 7.78	0.109	60.42 ± 7.94	59.71 ± 9.49	0.741
Gender			0.999			0.69
Male	16	61		6	23
Female	18	65		15	40
Smoking status			0.375			0.276
Smoker	7	38		4	22
Nonsmoker	27	88		17	41
CT-reported tumor size, mean ± SD (cm)	3.04 ± 0.93	2.25 ± 0.81	0.001	3.36 ± 0.92	2.29 ± 0.82	0.001
Clinical T stage			0.001			0.001
cT1	16	99		4	53
cT2	18	27		17	10
Histological grade			0.999			0.001
Highly/Moderately differentiated	31	112		10	61
Poorly differentiated	3	14		11	2
Tumor location			0.507			0.999
Upper	18	77		11	31
Middle- Lower	16	49		10	32
Tumor type			0.014			0.012
Central	18	36		11	13
Peripheral	16	90		10	50
Pleural traction			0.873			0.999
Yes	18	71		17	51
No	16	55		4	12
Visceral pleural invasion			0.092			0.115
Yes	21	55		17	37
No	13	71		4	26
CEA level			0.003			0.112
Normal	16	95		16	58
Abnormal	18	31		5	5
Wedge resection			0.413			0.104
Yes	3	20		0	9
No	31	106		21	54
Rad score, median	8.91	5.94	0.001	6.12	4.96	0.001

Note: Significant values are printed in bold.

Abbreviations: SD, standard deviation; OLN, occult lymph node; CEA, carcinoembryonic antigen.

Figure 2 Radiomics feature reduction and selection using the least absolute shrinkage and selection operator (LASSO) binary model. (A) Selection by the LASSO model utilized 10-fold cross-validation with the minimum criteria. The binomial deviances were plotted against log (lambda). Dotted vertical lines mark the optimal value applying the minimum criteria with 1 standard error (the 1-SE criteria). The optimal λ value of 0.101 with log(λ) =−2.291 was selected. (B) LASSO coefficient profiles of the 357 radiomics features. The vertical line was delineated at the optimal value by 10-fold cross-validation, and 3 features were chosen with nonzero coefficients in the plot.

Table 2 The Results of ROC Analysis

Training Cohort	AUC (95% CI)	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	F1 Score (%)
Tumor size	0.730 (0.634–0.827)	76.5	61.9	35.1	90.7	48.1
Rad score	0.767 (0.680–0.855)	76.5	64.3	36.6	91.0	49.5
Nomogram	0.782 (0.691–0.874)	61.8	87.3	56.8	89.4	59.2
Validation cohort
Tumor size	0.807 (0.706–0.885)	80.9	82.5	60.7	92.9	69.4
Rad score	0.767 (0.662–0.853)	80.9	68.3	45.9	91.5	58.6
Nomogram	0.813 (0.713–0.890)	80.9	84.1	63.0	93.0	70.9

Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value.

Figure 3 ROC curves and calibration curves. (A) ROC curves for training cohort. (B) ROC curve for radiomics signature of validation cohort. (C) Calibration curve of the radiomics model in the training group. (D) Calibration curve of the radiomics model in the validation group. Calibration curves described the calibration of each predictive model as a measure of the agreement between the predicted probabilities of occult lymph node metastasis (OLNM) and the observed result. The y-axis shows the actual OLNM rate, and the x-axis shows the predicted OLNM risks. The diagonal dotted blue line denotes a perfect prediction (ideal model). The red line reveals the actual performance of the nomogram.

Table 3 Univariable and Multivariable Logistic Regression Analyses for OLN

Variable and Intercept	Univariable Logistic Regression		Multivariable Logistic Regression
	OR (95% CI)	P value	OR (95% CI)	P value
Smoking (yes or no)	0.600 (0.241–1.498)	0.274	NA	NA
KPS score	0.886 (0.361–2.179)	0.793	NA	NA
CT-reported tumor size	2.743 (1.706–4.411)	0.001	1.921 (1.114–3.311)	0.019
T stage (T1 or T2)	4.125 (1.860–9.149)	0.001	0.732 (0.171–3.138)	0.675
Tumor location (upper or middle-lower)	0.716 (0.334–1.535)	0.39	NA	NA
Tumor type (central or peripheral)	2.812 (1.294–6.114)	0.009	1.603 (0.657–3.910)	0.3
Wedge resection (yes or no)	0.513 (0.143–1.841)	0.306	NA	NA
Histologic grade (poor or moderate/high)	1.292 (0.349–4.782)	0.702	NA	NA
Histologic subtype (acinar or not)	0.869 (0.385–1.963)	0.736
Pleural traction (yes or no)	0.871 (0.408–1.863)	0.723	NA	NA
Visceral pleural invasion (yes or no)	2.085 (0.960–4.531)	0.063	NA	NA
CEA level (normal or abnormal)	3.085 (1.401–6.790)	0.005	1.966 (0.801–4.825)	0.14
Rad score (per 0.1 increase)	1.438 (1.221–1.694)	0.001	1.318 (1.107–1.571)	0.002

Note: Significant values are printed in bold.

Abbreviations: OR, odds ratio; KPS, Karnofsky performance status; CT, computed tomography; CEA, carcinoembryonic antigen.

Figure 4 Radiomics nomogram. The predictive nomogram was built in the training group. Its variables were CT-reported tumor size and Rad-score.

Figure 5 Decision curve analysis for the radiomics nomogram in the validation cohort. The y-axis measures the net benefit, and the x-axis represents the threshold probability. The red line represents the radiomics nomogram. The blue line represents the assumption that all patients have OLNM. The green line represents the assumption that no patients have OLNM. The decision curve in the validation cohort indicates that if the threshold probability of a patient was between 0.22 and 0.83, the nomogram to predict OLNM showed more benefit than the treat-all therapeutic plan or treat-none plan.