A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Demographics of the study cohort

A total of 656 participants (681 SSNs) were enrolled, including 241 males and 415 females, with a mean age of 52.03 ± 12.26. There were 546 never smokers and 110 current or former smokers. Moreover, 407 patients (423 SSNs) operated between April 2019 and April 2020 were into the derivation cohort and 249 patients (258 SSNs) operated between May 2020 and December 2020 were assigned into the validation cohort. The baseline clinical characteristics and CT features of SSNs are shown in Tables 1 and 2, respectively. In derivation cohort, 110 SSNs were diagnosed as precursor glandular lesions (AAH =7, AIS =103), and 313 SSNs were diagnosed as adenocarcinomas (MIA =144, IAC =169). There were significant differences between precursor glandular lesions and adenocarcinomas subgroups in the lesion size (P <0.001), mean CT value (P <0.001), volume (P <0.001), mass (P <0.001), vascular change (P <0.001), bronchiole change (P <0.001), lobulation (P <0.001), pleural attachment (P <0.001), spiculation (P =0.032), and lesion-lung interface (P <0.001), except for bubble (P =0.081). In validation cohort, 72 SSNs were diagnosed as precursor glandular lesions (AAH =4, AIS =68), and 186 SSNs were diagnosed as adenocarcinomas (MIA =89, IAC =97). There were significant differences between precursor glandular lesions and adenocarcinomas subgroups in the lesion size (P <0.001), mean CT value (P <0.001), volume (P <0.001), mass (P <0.001), vascular change (P <0.001), bronchiole change (P =0.003), lobulation (P <0.001), pleural attachment (P =0.006), lesion-lung interface (P =0.003), except for bubble (P =0.359), and spiculation (P =0.092). The CT features and pathological results are shown in Table 3. The CT and pathological images from the 2 examples are shown in Fig. 2.

Table 1 Baseline clinical characteristics of patients with SSNsTable 2 CT features of SSNs in the derivation and validation cohortsTable 3 CT features and pathological results of SSNs in derivation and validation cohortsFig. 2figure 2

a-d CT and pathological images of one adenocarcinoma in situ (AIS) appearing as subsolid nodule (SSN). a CT multiplanar reconstruction (MPR) and b volume rending technique (VRT) showing the absence of vascular change, and lobulation (axial and coronal). c The long axis, short axis, and mean CT value were calculated using the semi-automated segmentation tool. d Pathology: the tumor cells were attached to the alveolar wall; the basement membrane was intact. (HE staining ×100). e-h CT and pathological images of one invasive adenocarcinoma (IAC) appearing as SSN. e CT MPR and f maximal intensity projection (MIP) showing the presence of vascular change, and lobulation (axial and sagittal). g The long axis, short axis, and mean CT value were calculated using the semi-automated segmentation tool. h Pathology: the tumor cells damaged alveolar cells, a large number of tumor cells infiltrating the interstitium. (HE staining ×100)

Interobserver and intraobserver agreements

Intraobserver and intraobserver agreements between three radiologists were near perfect, the ICC values of quantitative parameters and kappa coefficients of categorical variables were all greater than 0.75 (supplemental Tables 1 and 2).

Screening for independent risk factors

Univariate analysis of the derivation cohort indicated lesion size (OR =1.373; 95% CI, 1.061-1.777, P =0.016), mean CT value (OR =1.005; 95% CI, 1.001-1.010, P =0.024), vascular change (OR =5.125; 95%CI, 1.437-18.281, P =0.012), lobulation (OR =6.196; 95%CI, 2.007-19.127, P =0.002), and spiculation (OR =2.436; 95%CI, 1.055-5.625, P =0.037) correlated with adenocarcinomas. However, volume, mass, bronchiole change, bubble, pleural attachment, and lesion-lung interface did not correlate with adenocarcinomas (P >0.05). Stepwise multivariate analysis showed that lesion size (OR =1.335; 95% CI, 1.178-1.512, P <0.001), mean CT value (OR =1.005; 95% CI, 1.002-1.008, P =0.002), vascular change (OR =5.771; 95% CI, 1.659-20.074, P =0.006), and lobulation (OR =6.528; 95% CI, 2.173-19.608, P =0.001) were independent risk factors for adenocarcinomas (Table 4).

Table 4 Univariate and multivariate logistic analysis of CT features for adenocarcinomasConstruction of the nomogram model

Based on univariate and multivariate logistic regression analysis results, an individualized nomogram was generated by incorporating the 4 independent risk factors, namely lesion size, mean CT value, vascular change and lobulation (Fig. 3a). The nomogram showed that lesion size was the most important contributor to discrimination, followed by mean CT value, lobulation, and vascular change. Each independent risk factor in the nomogram was assigned a point based on regression coefficient and a straight line drawn based on total points. Finally, the probabilities of individual values were determined using the function conversion relationship of total points (Fig. 3b).

Fig. 3figure 3

a A nomogram for predicting the probability of adenocarcinomas in patients with subsolid nodules (SSNs). b A SSN with a lesion size of 12.5mm, mean CT value -627 HU, vascular change (-), lobulation (+). The total points of SSN was 131, and the probability of adenocarcinomas was 0.945

Validation and calibration of the nomogram

In the derivation cohort, the C-index of the nomogram in predicting adenocarcinomas was 0.867 (95% CI, 0.833-0.901) which exceeded that of the lesion size (C-index =0.779; 95% CI, 0.733-0.825), mean CT value (C-index =0.740; 95% CI, 0.688-0.793), vascular change (C-index =0.723; 95% CI, 0.691-0.754), and lobulation (C-index =0.734, 95% CI, 0.701-0.767) (Fig. 4a, Table 5). Lesion size 8.5mm and mean CT value -579.5 HU were the optimal threshold values for adenocarcinomas.

Fig. 4figure 4

Roc curves of the nomogram and independent risk factors in the derivation cohort and validation cohort of adenocarcinomas. a Derivation cohort. b Validation cohort

Table 5 The C-indexes of the nomogram and variables from the logistic regression algorithm in the derivation and validation cohorts

Furthermore, the C-index of 0.877 (95% CI, 0.836-0.917) indicated that the nomogram had good discrimination in the validation cohort (Fig. 4b, Table 5). Evaluation of the nomograms’ performance using calibration curves, with the 45-degree line indicating best performance, revealed that the predicted results were strongly consistent with the actual results in both derivation and validation cohorts (Fig. 5a, d). Decision curve analysis of the nomogram’s value and clinical impact curve analysis revealed that the nomogram had good standardized net benefit and prediction performance (Fig. 5b, c, e, f).

Fig. 5figure 5

Analysis of the prediction performance of the nomogram in the a-c Derivation cohort and d-f Validation cohort. a, d Calibration curve, b, e Decision curve, c, f Clinical impact curve

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