Clinicopathological models for predicting lymph node metastasis in patients with early-stage lung adenocarcinoma: the application of machine learning algorithms

Peritumoral and intratumoral radiomics for predicting visceral pleural invasion in lung adenocarcinoma based on preoperative computed tomography (CT)

AIM

To evaluate the prediction of peritumoral and intratumoral radiomics for visceral pleural invasion (VPI) in lung adenocarcinoma cancer (LAC) based on preoperative computed tomography (CT) radiomics.

MATERIALS AND METHODS

In total, 350 patients with LAC confirmed by surgery pathology were enrolled in The Second Hospital of Hebei Medical University, including 281 VPI negative patients and 69 VPI positive patients, were divided into the training cohort (n = 280) and validation cohort (n=70) at random with a ratio of 8:2. We extracted the radiomics features from the 3 region of interest (ROI), including gross tumor volume (GTV), the gross peritumoral tumor volume (GPTV) and the gross volume of the tumor rim (included the outer 4 mm of the tumor and 4mm of the tumor adjacent lung tissue on either side of the tumor contour boundary, GTR).The maximal redundancy minimal relevance (mMRM) algorithm and the least absolute shrinkage and selection operator (LASSO) was performed to reduce feature dimensionality and the radiomics score (Rad score) of the best radiomics model was combined with CT morphological characteristics with statistical significance in the univariable analysis to construct the combined model. The performance of the models was evaluated based on receiver operating characteristics (ROC) curve, calibration, and clinical usefulness. DeLong's test was used to assess differences in area under curve (AUC) between different models.

RESULTS

There were no statistically significant differences in patient's gender, age, and BMI between the VPI positive group and VPI negative group (all p＞0.05). There were statistically significant differences in the tumor maximum diameter, tumor CT image type, vacuole sign, and pleural indentation sign between the VPI positive group and VPI negative group (all p < 0.05). The models of radiomics of GTV, GPTV, and GTR showed high predictive value in the training cohort (All AUC > 0.75). Compared with GTV, GTR radiomics models, the GPTV radiomics model constructed via the logistic regression (LR) method exhibited better prediction performance with the AUCs of 0.819, 0.827; accuracy of 0.757,0.743; sensitivity of 0.800,0.786; specificity of 0.747,0.732 in the training and validation cohorts, respectively. The LR model of GPTV radiomics was defined as the optimal model for predicting VPI, since its excellent performance in both ROC, calibration curve and decision curve analysis (DCA).

CONCLUSION

Preoperative CT-based radiomics models can predict VPI in patients with LAC; the LR algorithm combined the GPTV radiomics was the optimal choice, demonstrating high sensitivity, specificity, accuracy and clinical usefulness.

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.

Prediction of Distant Metastases After Stereotactic Body Radiation Therapy for Early Stage NSCLC: Development and External Validation of a Multi-Institutional Model

INTRODUCTION

Distant metastases (DMs) are the primary driver of mortality for patients with early stage NSCLC receiving stereotactic body radiation therapy (SBRT), yet patient-level risk is difficult to predict. We developed and validated a model to predict individualized risk of DM in this population.

METHODS

We used a multi-institutional database of 1280 patients with cT1-3N0M0 NSCLC treated with SBRT from 2006 to 2015 for model development and internal validation. A Fine and Gray (FG) regression model was built to predict 1-year DM risk and compared with a random survival forests model. The higher performing model was evaluated on an external data set of 130 patients from a separate institution. Discriminatory performance was evaluated using the time-dependent area under the curve (AUC). Calibration was assessed graphically and with Brier scores.

RESULTS

The FG model yielded an AUC of 0.71 (95% confidence interval [CI]: 0.57-0.86) compared with the AUC of random survival forest at 0.69 (95% CI: 0.63-0.85) in the internal test set and was selected for further testing. On external validation, the FG model yielded an AUC of 0.70 (95% CI: 0.57-0.83) with good calibration (Brier score: 0.08). The model identified a high-risk patient subgroup with greater 1-year DM rates in the internal test (20.0% [3 of 15] versus 2.9% [7 of 241], p = 0.001) and external validation (21.4% [3 of 15] versus 7.8% [9 of 116], p = 0.095). A model nomogram and online application was made available.

CONCLUSIONS

We developed and externally validated a practical model that predicts DM risk in patients with NSCLC receiving SBRT which may help select patients for systemic therapy.

Minor histological components predict the recurrence of patients with resected stage I acinar- or papillary-predominant lung adenocarcinoma

Objective

Invasive lung adenocarcinoma is composed of five different histological subgroups with diverse biological behavior and heterogeneous morphology, the acinar/papillary-predominant lung adenocarcinomas are the most common subgroups and recognized as an intermediate-grade group. In the real world, clinicians primarily consider predominant patterns and ignore the impact of minor components in the prognosis of lung adenocarcinoma. The study evaluated the clinicopathologic characteristics of the lepidic, solid, and micropapillary patterns as non-predominant components and whether the minimal patterns had prognostic value on acinar/papillary-predominant lung adenocarcinomas.

Methods

A total of 153 acinar/papillary-predominant lung adenocarcinoma patients with tumor size ≤4 cm were classified into four risk subgroups based on the presence of lepidic and micropapillary/solid components: MP/S^-Lep⁺, MP/S⁺Lep⁺, MP/S^-Lep^-, and MP/S⁺Lep^- groups. The Cox-proportional hazard regression model was used to assess disease-free survival (DFS).

Results

The risk subgroups based on the non-predominant patterns were associated with differentiation (P = 0.001), lymphovascular invasion (P = 0.001), and recurrence (P = 0.003). In univariate analysis, DFS was correlated with non-predominant components (P = 0.014), lymphovascular invasion (P = 0.001), carcinoembryonic antigen (CEA) (P = 0.001), and platelet-to-lymphocyte ratio (PLR) (P = 0.012). In the multivariate analysis, non-predominant components (P = 0.043) and PLR (P = 0.032) were independent prognostic factors for DFS. The 5-year survival rates of MP/S^-Lep⁺, MP/S⁺Lep⁺, MP/S^-Lep^- and MP/S⁺Lep^- subgroups were 93.1%,92.9%,73.1%,61.9%, respectively. The MP/S^-Lep⁺ subgroup had the favorable prognosis than MP/S⁺Lep^- subgroup with a statistically significant difference (P = 0.002). As minor components, the lepidic patterns were a protective factor, and the solid and micropapillary components were poor factors. The recurrence was related to the presence of non-predominant patterns rather than their proportion. Adjuvant chemotherapy did not significantly improve the prognosis of the MP/S⁺Lep^- subgroup (P = 0.839).

Conclusions

Regardless of the proportion, the presence of micropapillary/solid components and the absence of lepidic patterns are aggressive factors of DFS in patients with resected stage I acinar- or papillary-predominant lung adenocarcinoma.

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

SIMPLE SUMMARY

Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.

BACKGROUND

Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.

METHODS

Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.

RESULTS

The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.

CONCLUSIONS

The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

The development of machine learning in lung surgery: A narrative review

Background

Machine learning reflects an artificial intelligence that allows applications to improve their accuracy to predict outcomes, eliminating the need to conduct explicit programming on them. The medical field has increased its focus on establishing tools for integrating machine learning algorithms in laboratory and clinical settings. Despite their importance, their incorporation is minimal in the medical sector yet. The primary goal of this study is to review the development of machine learning in the field of thoracic surgery, especially lung surgery.

Methods

This article used the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA). The sources used to gather data are the PubMed, Cochrane, and CINAHL databases and the Google Scholar search engine.

Results

The study included 19 articles, where ten concentrated on the application of machine learning in especially lung surgery, six focused on the benefits and limitations of machine learning algorithms in lung surgery, and three provided an overview of the future of machine learning in lung surgery.

Conclusion

The outcome of this study indicates that the field of lung surgery has attempted to integrate machine learning algorithms. However, the implementation rate is low, owing to the newness of the concept and the various challenges it encompasses. Also, this study reveals the absence of sufficient literature discussing the application of machine learning in lung surgery. The necessity for future research on the topic area remains evident.