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

Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening

The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules' (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the PET examination, and the ultimate outcome (benign/malignant), determined by patient follow-up or biopsy, was used to build the ML classifier. Two medical experts provided their malignancy likelihood scores, taking into account the patient's clinical condition and without prior knowledge of the true label of the SPN. Incorporating human assessments into ML model training improved diagnostic efficiency by approximately 3%, highlighting the synergistic role of human judgment alongside ML. Under the latter setup, the ML model had an accuracy score of 95.39% (CI 95%: 95.29-95.49%). While ML exhibited swings in probability scores, human readers excelled in discerning ambiguous cases. ML outperformed the best human reader in challenging instances, particularly in SPNs with ambiguous probability grades, showcasing its utility in diagnostic grey zones. The best human reader reached an accuracy of 80% in the grey zone, whilst ML exhibited 89%. The findings underline the collaborative potential of ML and human expertise in enhancing SPN characterization accuracy and confidence, especially in cases where diagnostic certainty is elusive. This study contributes to understanding how integrating ML and human judgement can optimize SPN diagnostic outcomes, ultimately advancing clinical decision-making in PET/CT screenings.

Repeated pulmonary nodules as the primary symptom of familial hemophagocytic lymphohistiocytosis in adults: a case report and review

Pulmonary nodules are usually considered to be associated with malignant tumors and benign lesions, such as granuloma, pulmonary lymph nodes, fibrosis, and inflammatory lesions. Clinical cases of pulmonary nodules associated with hemophagocytic lymphohistiocytosis have rarely been reported. Therefore, when patients develop pulmonary nodules, the possibility of developing hemophagocytic lymphohistiocytosis is often not considered. We report the first case of familial hemophagocytic lymphohistiocytosis with recurrent pulmonary nodules as the first symptom. Our findings will hopefully provide new ideas for the diagnosis and treatment of pulmonary nodules in the future.

Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76-81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied-functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery.

Prediction of single pulmonary nodule growth by CT radiomics and clinical features - a one-year follow-up study

Background

With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules.

Materials and methods

According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis.

Results

There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively.

Conclusions

In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.

Sociodemographic and clinical characteristics associated with never-smoking status in patients with lung cancer: findings from a large integrated health system

Background

Evidence is limited characterizing sociodemographically diverse patient populations with lung cancer in relation to smoking status.

Methods

In a cross-sectional analysis of adults diagnosed with lung cancer at ages ≥30 years from 2007-2018 within an integrated healthcare system, overall and sex-specific prevalence of never smoking were estimated according to sociodemographic and clinical characteristics. Adjusted prevalence ratio (aPR) and 95% confidence interval (CI) were also estimated using modified Poisson regression to identify patient characteristics associated with never smoking, overall and by sex. Similar analyses were conducted to explore whether prevalence and association patterns differed between non-Hispanic White and Asian/Pacific Islander patients.

Results

Among 17,939 patients with lung cancer, 2,780 (15.5%) never smoked and 8,698 (48.5%) had adenocarcinoma. Overall prevalence of never smoking was higher among females than males (21.2% vs. 9.2%, aPR 2.13, 95% CI: 1.98-2.29); Asian/Pacific Islander (aPR 2.85, 95% CI: 2.65-3.07) and Hispanic (aPR 1.72, 95% CI: 1.51-1.95) than non-Hispanic White patients; patients who primarily spoke Spanish (aPR 1.60, 95% CI: 1.32-1.94), any Asian language (aPR 1.20, 95% CI: 1.10-1.30), or other languages (aPR 1.84, 95% CI: 1.27-2.65) than English; patients living in the least vs. most deprived neighborhoods (aPR 1.36, 95% CI: 1.24-1.50); and patients with adenocarcinoma (aPR 2.57, 95% CI: 2.18-3.03), other non-small cell lung cancer (NSCLC) (aPR 2.00, 95% CI: 1.63-2.45), or carcinoid (aPR 3.60, 95% CI: 2.96-4.37) than squamous cell carcinoma tumors. Patterns of never smoking associated with sociodemographic, but not clinical factors, differed by sex. The higher prevalence of never smoking associated with Asian/Pacific Islander race/ethnicity was more evident among females (aPR 3.30, 95% CI: 2.95-3.47) than males (aPR 2.25, 95% CI: 1.92-2.63), whereas the higher prevalence of never smoking associated with living in the least deprived neighborhoods was more evident among males (aPR 1.93, 95% CI: 1.56-2.38) than females (aPR 1.18, 95% CI: 1.06-1.31). Associations between primary language and never-smoking status were found only among females. Overall and sex-specific prevalence and association patterns differed between Asian/Pacific Islander and non-Hispanic white patients.

Conclusions

Our findings suggest that patterns of never-smoking status associated with sociodemographic and clinical characteristics are different across sex and race/ethnicity among patients with lung cancer. Such data are critical to increasing awareness and expediting diagnosis of this disease.

Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant (P < 0.05). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant (P < 0.05). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance (P < 0.05). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant (P < 0.05). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [¹⁸F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([¹⁸F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [¹⁸F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [¹⁸F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [¹⁸F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.