<p>Nomogram For The Prediction Of Malignancy In Small (8–20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations</p>

Abstract

PurposeThis study aimed to develop and validate a nomogram for predicting the malignancy of small (8–20 mm) solid indeterminate solitary pulmonary nodules (SPNs) in a Chinese population by using routine clinical and computed tomography data.MethodsThe prediction model was developed using a retrospective cohort that comprised 493 consecutive patients with small indeterminate SPNs who were treated between December 2012 and December 2016. The model was independently validated using a second retrospective cohort comprising 216 consecutive patients treated between January 2017 and May 2018. The investigated variables included patient characteristics (e.g., age and smoking history), nodule parameters (e.g., marginal spiculation and significant enhancement), and tumor biomarker levels (e.g., carcinoembryonic antigen). A prediction model was developed by using multivariable logistic regression analysis, and the model’s performance was presented as a nomogram. The model was evaluated based on its discriminative ability, calibration, and clinical usefulness.ResultsThe developed nomogram was ultimately based on age, marginal spiculation, significant enhancement, and pleural indentation. The Harrell concordance index values were 0.869 in the training cohort (95% confidence interval: 0.837–0.901) and 0.847 in the validation cohort (95% confidence interval: 0.792–0.902). The Hosmer-Lemeshow test revealed good calibration in each of the training and validation cohorts. Decision curve analysis confirmed that the nomogram was clinically useful (risk threshold from 0.10 to 0.85).ConclusionPatient age, marginal spiculation, significant enhancement, and pleural indentation are independent predictors of malignancy in small indeterminate solid SPNs. The developed nomogram is easy-to-use and may allow the accurate prediction of malignancy in small indeterminate solid SPNs among Chinese patients.

Solitary Pulmonary Nodule Malignancy Predictive Models Applicable to Routine Clinical Practice: A Systematic Review&nbsp;

“…Only one study (17) included exposure to asbestos, none included exposure to radon or uranium, and only one (23) included passive exposure to tobacco smoke. Emphysema was collected in 2 studies (21,24) , and family history of lung cancer was collected in only one (21). Only one study (23) described peri ssural nodules, and two studies (23,26) included multiple nodules.…”

<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

“…30 Besides, another three models were also established for solid nodules based on a Chinese population (AUC=0.85~0.87). [31][32][33] Compared to these four models, the current solid-nodule models shared some similar risk factors, such as age, sex, history of malignancy, morphology, and serum CEA, but some novel markers like V25, RV/TLC, serum lymphocyte, CYFRA21-1, and NSE were also identified. As for model performance, these four were a little better than our models (AUC=0.82), which can be associated with some valuable predictors they enrolled, such as enhancement, VDT, as well as maximum uptake value of nodules.…”

Computed tomography‐based spiculated sign for prediction of malignancy in lung nodules: A meta‐analysis

“…28 Many prior studies have relied upon morphological findings such as the size, location, cavitation, calcification, spiculated sign and pleural indentation to accurately diagnose LNs. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] In one prior large multi-center study, larger lesion size, evidence of spiculated sign and pleural indentation were all considered to be indicative of a higher risk of LN malignancy, whereas calcification was predictive of an LN being benign. 20 The reported diagnostic utility of CT-based spiculated sign varies significantly among prior studies and as such a meta-analysis is warranted in order to better understand the true diagnostic utility of this morphological parameter with F I G U R E 2 Methodological quality (A) graph and (B) summary greater statistical power.…”

“…This may be due to some benign LNs having been confirmed via CT follow-up in certain European studies, 8,14,21 whereas the majority of Chinese studies confirmed LN status via surgical resection. 9,12,13,[17][18][19][22][23][24][25] There are certain limitations to the present analysis. For one, all included studies were retrospective in nature.…”

“…6,7 Multiple predictive models have been designed to date in an effort to identify malignant LNs. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] While these different models generally incorporate a range of different risk factors, CT-based spiculated sign, which is a malignancy risk factor, is often incorporated into such models. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] In the present meta-analysis, we evaluated the diagnostic utility of CT-based spiculated sign as a means of differentiating between malignant and benign LNs.…”