Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography

PurposeTo establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT).MethodA total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility.ResultsFor the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83–0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 vs. 66%, P<0.01; 87 vs. 61%, P<0.01; 85 vs. 66%, P=0.03, in the train, validation, and test sets, respectively). In addition, while there was a slight increase in sensitivity, the specificity improved significantly by an average of 42% (the corresponding improvements in the three sets ranged from 43, 33, and 42% to 82, 78, and 84%, respectively; P<0.01 for all).ConclusionThe CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

Background Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice. Methods We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles. Results A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice. Conclusions The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management. Systematic review registration PROSPERO CRD42020161559

Update on Pulmonary Ossifications in the Differential Diagnosis of Solitary Pulmonary Nodules

Pulmonary ossifications have often been regarded as rare, post-mortem findings without any clinical significance. We have investigated the occurrence of pulmonary ossifications in patients undergoing thoracic procedures, and how this may affect the differential diagnosis of solitary pulmonary nodules. In addition, we have performed a literature search on the occurrence and possible pathogenesis of these ossifications. From January 2008 until August 2019, we identified pulmonary ossifications in 34 patients who underwent elective pulmonary surgery. Pre-operative imaging was unable to differentiate these ossifications from solid tumors. A definitive diagnosis was made by an experienced pathologist (VS, ML). The PubMed database was researched in December 2019 with the search terms “pulmonary ossifications”; “heterotopic ossifications”; and “solitary pulmonary nodule”. In total, 27 patients were male, with a mean age of 63 ± 12 years (age 41 to 82 on diagnosis). All lesions were identified on thoracic CT and marked for resection by a multidisciplinary team. A total of 17 patients were diagnosed with malignancy concurrent with ossifications. There was a clear predilection for the right lower lobe (12 cases, 35.3%) and most ossifications had a nodular form (70.6%). We could not identify a clear association with any other pathology, either cancerous or non-cancerous in origin. Oncologic or pulmonary comorbidities did not influence patient survival. Pulmonary ossifications are not as seldom as thought and are not just a curiosity finding by pathologists. These formations may be mistaken for a malignant space-occupying lesion, both pre-and perioperatively, as they are indistinguishable in imaging. We propose these ossifications as an underestimated addition to the differential diagnosis of a solitary pulmonary nodule.

Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study

Objectives To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. Methods This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. Results The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0–65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. Conclusions This study demonstrated that a nomogram constructed by identified clinical–radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key Points • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.