Revisions to the Tumor, Node, Metastasis staging of lung cancer (8th edition): Rationale, radiologic findings and clinical implications

The Society of Thoracic Surgeons Expert Consensus on the Multidisciplinary Management and Resectability of Locally Advanced Non-small Cell Lung Cancer

BACKGROUND

The contemporary management and resectability of locally advanced lung cancer are undergoing significant changes as new data emerge regarding immunotherapy and targeted treatments. The objective of this document is to review the literature and present consensus among a group of multidisciplinary experts to guide the determination of resectability and management of locally advanced non-small cell lung cancer (NSCLC) in the context of contemporary evidence.

METHODS

The Society of Thoracic Surgeon Workforce on Thoracic Surgery assembled a multidisciplinary expert panel composed of thoracic surgeons and medical and radiation oncologists with established expertise in the management of lung cancer. A focused literature review was performed, and expert consensus statements were developed using a modified Delphi process to address 3 major themes: (1) assessing resectability and multidisciplinary management of locally advanced lung cancer, (2) neoadjuvant (including perioperative) therapy, and (3) adjuvant therapy.

RESULTS

A consensus was reached on 19 recommendations. These consensus statements reflect updated insights on resectability and multidisciplinary management of locally advanced lung cancer based on the latest literature and current clinical experience, mainly focusing on the appropriateness of surgical therapy and emerging data regarding neoadjuvant and adjuvant therapies.

CONCLUSIONS

Despite the complex decision-making process in managing locally advanced lung cancer, this expert panel agreed on several key recommendations. This document provides guidance for thoracic surgeons and other medical professionals in the optimal management of locally advanced lung cancer based on the most updated evidence and literature.

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BACKGROUND

This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.

METHODS

Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong's test was used to compare the CPIs group with the VMIs group.

RESULTS

When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong's test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).

CONCLUSION

The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Value of Presurgical 18F-FDG PET/CT Radiomics for Predicting Mediastinal Lymph Node Metastasis in Patients with Lung Adenocarcinoma

Objective: The aim of this study was to develop a 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic model for predicting mediastinal lymph node metastasis (LNM) in presurgical patients with lung adenocarcinoma. Methods: The study enrolled 320 patients with lung adenocarcinoma (288 internal and 32 external cases) and extracted 190 radiomic features using the LIFEx package. Optimal radiomic features to build a radiomic model were selected using the least absolute shrinkage and selection operator algorithm. Logistic regression was used to build the clinical and complex (combined radiomic and clinical variables) models. Results: Ten radiomic features were selected. In the training group, the area under the receiver operating characteristic curve of the complex model was significantly higher than that of the radiomic and clinical models [0.924 (95% CI: 0.887-0.961) vs. 0.863 (95% CI: 0.814-0.912; p = 0.001) and 0.838 (95% CI: 0.783-0.894; p = 0.000), respectively]. The sensitivity, specificity, accuracy, and positive and negative predictive values of the radiomic model were 0.857, 0.790, 0.811, and 0.651 and 0.924, respectively, which were better than that of visual evaluation (0.539, 0.724, 0.667, and 0.472 and 0.775, respectively) and PET semiquantitative analyses (0.619, 0.732, 0.697, and 0.513 and 0.808, respectively). Conclusions: 18F-FDG PET/CT radiomics showed good predictive performance for LNM and improved the N-stage accuracy of lung adenocarcinoma.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

The survival and cost-effectiveness analysis of adjunctive Chinese medicine therapy for patients with non-small cell lung cancer: a nationwide cohort study in Taiwan

Introduction: Cancer, particularly lung cancer, is a significant global healthcare challenge. Non-Small Cell Lung Cancer (NSCLC) constitutes 85% of cases. Patients often seek alternative therapies like Chinese medicine alongside Western treatments. This study investigates the survival outcomes and cost-effectiveness of adjunctive Chinese medicine therapy for NSCLC patients in Taiwan. Methods: We utilized the National Health Insurance Research Database in a retrospective cohort study from 2000 to 2018, focusing on NSCLC patients diagnosed between 2007 and 2013. After propensity score matching 1:5 ratio, then compared patients with and without adjunctive Chinese medicine therapy. Survival outcomes, cost-effectiveness, and sensitivity analyses were conducted. Results: The study involved 43,122 NSCLC patients with 5.76% receiving adjunctive Chinese medicine. There is no significant associated between the risk of death and adjuvant Chinese medicine therapy until 181-365 days of adjuvant treatment could reduce the risk of death (HR = 0.88, 95% CI: 0.80-0.98). Cost-effectiveness analysis showed an incremental cost-effectiveness ratio of 880,908 NT$/year. Conclusion: Adjunctive Chinese medicine therapy, particularly when administered for 181-365 days, significantly reduced the mortality risk among stage IV NSCLC patients. The cost-effectiveness aligns with willingness-to-pay thresholds, indicating economic benefit.

[Comparison of Short-term Efficacy of Neoadjuvant Immunotherapy Combined with Chemotherapy and Surgery Alone for Locally Advanced Resectable Non-small Cell Lung Cancer]

BACKGROUND

Lung cancer is the cancer with the highest incidence and mortality rates in China, and non-small cell lung cancer (NSCLC) accounts for 80%-85% of all malignant lung tumors. Currently, surgical treatment remains the primary treatment modality for lung cancer. In recent years, the effectiveness of immune checkpoint inhibitors for NSCLC has become a consensus, and neoadjuvant immunochemotherapy (nICT) has shown promising efficacy and safety in early to intermediate stage NSCLC. However, there are fewer studies related to nICT for locally advanced NSCLC. This study aims to evaluate the efficacy and safety of nICT therapy in locally advanced resectable NSCLC.

METHODS

85 confirmed resectable stage IIIA and IIIB patients treated in the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from January 2021 to April 2024, were divided into the nICT group (n=32) and the surgery alone group (n=53). Clinical baseline data, perioperative indicators, postoperative complications, imaging response rate, pathological response rate, incidence of adverse events, and quality of life were compared between the two groups.

RESULTS

There were no statistically significant differences in clinical baseline data between the two groups (P>0.05). Incidence of choosing thoracotomy was higher in the nICT group than in the surgery alone group (P=0.002). There were no significant differences in surgical time, intraoperative blood loss, number of dissected lymph nodes, duration of chest tube placement, postoperative hospital stay, and R0 resection rate between the two groups (P>0.05). The overall incidence of postoperative complications was 31.25% in the nICT group and 22.64% in the surgery alone group, with no statistically significant difference (P=0.380). In the nICT group, the objective response rate (ORR) was 84.38%, with 5 cases of complete response (CR)(15.63%), 22 cases of partial response (PR)(68.75%), 15 cases of pathological response rate (pCR)(46.88%), and 11 cases of major pathological reaponse (MPR) (34.38%). During nICT treatment, 12 cases (37.50%) experienced grade 3 treatment-related adverse events, no death induced by adverse events or immune related adverse events. Moreover, the symptoms of the patients were improved after nICT treatment.

CONCLUSIONS

Neoadjuvant immunochemotherapy shows promising efficacy in locally advanced resectable NSCLC, with manageable treatment-related adverse events. It is a safe and feasible neoadjuvant treatment modality for locally advanced resectable NSCLC.

Recent Advancements in Minimally Invasive Surgery for Early Stage Non-Small Cell Lung Cancer: A Narrative Review

Introduction: Lung cancer remains a global health concern, with non-small cell lung cancer (NSCLC) comprising the majority of cases. Early detection of lung cancer has led to an increased number of cases identified in the earlier stages of NSCLC. This required the revaluation of the NSCLC treatment approaches for early stage NSCLC. Methods: We conducted a comprehensive search using multiple databases to identify relevant studies on treatment modalities for early stage NSCLC. Inclusion criteria prioritized, but were not limited to, clinical trials and meta-analyses on surgical approaches to early stage NSCLC conducted from 2021 onwards. Discussion: Minimally invasive approaches, such as VATS and RATS, along with lung resection techniques, including sublobar resection, have emerged as treatments for early stage NSCLC. Ground-glass opacities (GGOs) have shown prognostic significance, especially when analyzing the consolidation/tumor ratio (CTR). There have also been updates on managing GGOs, including the non-surgical approaches, the extent of lung resection indicated, and the level of lymphadenectomy required. Conclusions: The management of early stage NSCLC requires a further assessment of treatment strategies. This includes understanding the required extent of surgical resection, interpreting the significance of GGOs (specifically GGOs with a high CTR), and evaluating the efficacy of alternative therapies. Customized treatment involving surgical and non-surgical interventions is essential for advancing patient care.

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

Distinguishing recurrence from radiation-induced lung injury at the time of RECIST progressive disease on post-SABR CT scans using radiomics

Stereotactic ablative radiotherapy (SABR) is a highly effective treatment for patients with early-stage lung cancer who are inoperable. However, SABR causes benign radiation-induced lung injury (RILI) which appears as lesion growth on follow-up CT scans. This triggers the standard definition of progressive disease, yet cancer recurrence is not usually present, and distinguishing RILI from recurrence when a lesion appears to grow in size is critical but challenging. In this study, we developed a tool to do this using scans with apparent lesion growth after SABR from 68 patients. We performed bootstrapped experiments using radiomics and explored the use of multiple regions of interest (ROIs). The best model had an area under the receiver operating characteristic curve of 0.66 and used a sphere with a diameter equal to the lesion's longest axial measurement as the ROI. We also investigated the effect of using inter-feature and volume correlation filters and found that the former was detrimental to performance and that the latter had no effect. We also found that the radiomics features ranked as highly important by the model were significantly correlated with outcomes. These findings represent a key step in developing a tool that can help determine who would benefit from follow-up invasive interventions when a SABR-treated lesion increases in size, which could help provide better treatment for patients.

Symbiotic microbial communities in various locations of the lung cancer respiratory tract along with potential host immunological processes affected

Lung cancer has the highest mortality rate among all cancers worldwide. The 5-year overall survival rate for non-small cell lung cancer (NSCLC) is estimated at around 26%, whereas for small cell lung cancer (SCLC), the survival rate is only approximately 7%. This disease places a significant financial and psychological burden on individuals worldwide. The symbiotic microbiota in the human body has been significantly associated with the occurrence, progression, and prognosis of various diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis. Studies have demonstrated that respiratory symbiotic microorganisms and their metabolites play a crucial role in modulating immune function and contributing to the pathophysiology of lung cancer through their interactions with the host. In this review, we provide a comprehensive overview of the microbial characteristics associated with lung cancer, with a focus on the respiratory tract microbiota from different locations, including saliva, sputum, bronchoalveolar lavage fluid (BALF), bronchial brush samples, and tissue. We describe the respiratory tract microbiota's biodiversity characteristics by anatomical region, elucidating distinct pathological features, staging, metastasis, host chromosomal mutations, immune therapies, and the differentiated symbiotic microbiota under the influence of environmental factors. Our exploration investigates the intrinsic mechanisms linking the microbiota and its host. Furthermore, we have also provided a comprehensive review of the immune mechanisms by which microbiota are implicated in the development of lung cancer. Dysbiosis of the respiratory microbiota can promote or inhibit tumor progression through various mechanisms, including DNA damage and genomic instability, activation and regulation of the innate and adaptive immune systems, and stimulation of epithelial cells leading to the upregulation of carcinogenesis-related pathways.

Efficacy and safety of neoadjuvant tislelizumab combined with chemotherapy in locally advanced non-small cell lung cancer-a retrospective cohort study

Background

At present, comprehensive treatment is still the main approach for locally advanced non-small cell lung cancer (NSCLC) patients, and the research of neoadjuvant tislelizumab combined with chemotherapy in patients with locally advanced NSCLC is still in progress. We conducted this research in order to investigate the efficacy and safety of neoadjuvant tislelizumab combined with chemotherapy in the treatment of locally advanced NSCLC.

Methods

From January 1, 2021, to November 30, 2022, 12 patients with locally advanced NSCLC at the Fujian Medical University Union Hospital were retrospectively analyzed. All patients received three cycles of neoadjuvant immunotherapy combined with chemotherapy before surgery. The primary endpoint was pathological complete response (pCR), and the secondary endpoints were the objective response rate (ORR), R0 resection rate, and safety.

Results

According to the preoperative imaging evaluation, two patients (2/12, 16.67%) had complete remission, seven patients (7/12, 58.33%) had partial remission, and three patients (3/12, 25.00%) had stable disease. The overall objective remission rate was 75.0%. Postoperative pathology confirmed that seven patients (7/12, 58.33%) achieved pathological complete remission, and the R0 resection rate was 100%. During the treatment, five patients (5/12, 41.67%) had treatment-related adverse reactions, all of which were grade I-II according to the Common Terminology Criteria for Adverse Events (CTCAE) classification, and no adverse reactions of grade III or above were found.

Conclusions

Neoadjuvant tislelizumab combined with chemotherapy shows good efficacy and safety in patients with locally advanced NSCLC and has no significant adverse effects on perioperative outcomes. However, this is a small sample size study, and further large-scale prospective studies are needed in the future to validate our research results.

Evaluation of the relationship of the T and M stage with the erector spinae muscle area in male lung cancer patients

OBJECTIVES

Sarcopenia is very common due to cachexia and presents with a decrease in skeletal muscle mass. In this study, we aimed to investigate the relationship between the T, M category and the erector spinae muscle area (ESMa).

MATERIAL AND METHODS

The initial first thorax and high-resolution computed tomography (CT) of patients with lung cancer between 2015 and 2019 were retrospectively screened. After exclusion criterias remaining 226 male patients constituted the study group. ESMa was measured manually in the section taken from the T12 vertebra spinous process level as previously described in the literature and its relationship with the T and M stage were evaluated.

RESULTS

The mean ages of patients were 70 ± 9.57 years. The T stage was T1 in 34 (15%) patients, T2 in 46 (20.4%), T3 in 59 (26.1%), and T4 in 87 (38.5%). Metastasis was detected in 83 (36.7%) patients. The mean ESMa of the patients was 34.15 ± 7.21 mm² and did not differ according to the T stage (p = .39). ESMa was lower in the metastatic group (mean 30.42 ± 6.38 mm²) than the non-metastatic group (mean 36.32 ± 6.78 mm²) (p = .0001).

CONCLUSIONS

ESMa, one of the indicators of sarcopenia, is lower in patients with metastatic lung cancer than in nonmetastatic.

Effect of stage shift and immunotherapy treatment on lung cancer survival outcomes

OBJECTIVES

Non-small-cell lung cancer mortality has declined at a faster rate than incidence due to multiple factors, including changes in smoking behaviour, early detection which shifts diagnosis, and novel therapies. Limited resources require that we quantify the contribution of early detection versus novel therapies in improving lung cancer survival outcomes.

METHODS

Non-small-cell lung cancer patients from the Surveillance, Epidemiology, and End Results-Medicare data were queried and divided into: (i) stage IV diagnosed in 2015 (n = 3774) and (ii) stage I-III diagnosed in 2010-2012 (n = 15 817). Multivariable Cox-proportional hazards models were performed to assess the independent association of immunotherapy or diagnosis at stage I/II versus III with survival.

RESULTS

Patients treated with immunotherapy had significantly better survival than those who did not (HRadj: 0.49, 95% confidence interval: 0.43-0.56), as did those diagnosed at stage I/II versus stage III (HRadj: 0.36, 95% confidence interval: 0.35-0.37). Patients on immunotherapy had a 10.7-month longer survival than those who were not. Stage I/II patients had an average survival benefit of 34 months, compared to stage III. If 25%% of stage IV patients not on immunotherapy received it, there would be a gain of 22 292 person-years survival per 100 000 diagnoses. A switch of only 25% from stage III to stage I/II would correspond to 70 833 person-years survival per 100 000 diagnoses.

CONCLUSIONS

In this cohort study, earlier stage at diagnosis contributed to life expectancy by almost 3 years, while gains from immunotherapy would contribute ½ year of survival. Given the relative affordability of early detection, risk reduction through increased screening should be optimized.

Stage III Non-Small-Cell Lung Cancer: An Overview of Treatment Options

Lung cancer is the second-most commonly diagnosed cancer and the leading cause of cancer death worldwide. The most common histological type is non-small-cell lung cancer, accounting for 85% of all lung cancer cases. About one out of three new cases of non-small-cell lung cancer are diagnosed at a locally advanced stage—mainly stage III—consisting of a widely heterogeneous group of patients presenting significant differences in terms of tumor volume, local diffusion, and lymph nodal involvement. Stage III NSCLC therapy is based on the pivotal role of multimodal treatment, including surgery, radiotherapy, and a wide-ranging option of systemic treatments. Radical surgery is indicated in the case of hilar lymphnodal involvement or single station mediastinal ipsilateral involvement, possibly after neoadjuvant chemotherapy; the best appropriate treatment for multistation mediastinal lymph node involvement still represents a matter of debate. Although the main scope of treatments in this setting is potentially curative, the overall survival rates are still poor, ranging from 36% to 26% and 13% in stages IIIA, IIIB, and IIIC, respectively. The aim of this article is to provide an up-to-date, comprehensive overview of the state-of-the-art treatments for stage III non-small-cell lung cancer.

Clinical outcomes of immune checkpoint inhibitor therapy for advanced lung adenosquamous carcinoma

Background

Primary adenosquamous carcinoma (ASC) of the lung is a rare and aggressive disease and limited information is available on the efficacy of immune checkpoint inhibitors (ICIs) for this disease. Here, we evaluated the expression status of programmed death-1 ligand 1 (PD-L1) and efficacy of ICIs in patients with pulmonary ASC.

Methods

The efficacy and toxicity of ICIs were examined in 38 patients with previously treated lung ASC from November 2017 to October 2021 in Zhejiang Cancer Hospital (Hangzhou, China). Survival curves were plotted using the Kaplan-Meier method and the Cox proportional hazards model applied for univariate and multivariate analyses.

Results

A total of 38 patients with ASC were included in this retrospective study. ICI treatment induced an objective response rate (ORR) of 23.7% and a disease control rate (DCR) of 86.8%. The median progression-free survival (PFS) and median overall survival (OS) were 5.47 and 24.10 months, respectively. Seventeen patients were successfully evaluated for PD-L1 expression status, with 11 (64.7%) identified as PD-L1-positive. ORR and DCR for PD-L1-positive patients were 36.4% (4/11) and 100% (11/11) and the corresponding values for PD-L1-negative patients were 0 (0/6) and 50% (3/6), respectively. The median PFS of PD-L1-positive and PD-L1-negative patient groups was 5.00 and 1.90 months (P=0.166) while the median OS was 11.30 months and not reached, respectively (P=0.966). The incidence rate of immune-related adverse events (irAEs) was 52.6%, with 13.2% grade 3-4 irAEs. The most common irAEs were malaise and pneumonitis. One patient died of pneumonitis during the study.

Conclusions

ICIs show considerable potential as a treatment option for lung ASC. PFS and OS rates are similar for PD-L1-positive and PD-L1-negative patients. Further large-scale studies are required to establish the relationship between PD-L1 expression and response to ICIs in ASC.

Application of 18 F-fluorodeoxyglucose PET/CT radiomic features and machine learning to predict early recurrence of non-small cell lung cancer after curative-intent therapy

OBJECTIVE

To predict the recurrence of non-small cell lung cancer (NSCLC) within 2 years after curative-intent treatment using a machine-learning approach with PET/CT-based radiomics.

PATIENTS AND METHODS

A total of 77 NSCLC patients who underwent pretreatment 18 F-fluorodeoxyglucose PET/CT were retrospectively analyzed. Five clinical features (age, sex, tumor stage, tumor histology, and smoking status) and 48 radiomic features extracted from primary tumors on PET were used for binary classifications. These were ranked, and a subset of useful features was selected based on Gini coefficient scores in terms of associations with relapsed status. Areas under the receiver operating characteristics curves (AUC) were yielded by six machine-learning algorithms (support vector machine, random forest, neural network, naive Bayes, logistic regression, and gradient boosting). Model performances were compared and validated via random sampling.

RESULTS

A PET/CT-based radiomic model was developed and validated for predicting the recurrence of NSCLC during the first 2 years after curation. The most important features were SD and variance of standardized uptake value, followed by low-intensity short-zone emphasis and high-intensity zone emphasis. The naive Bayes model with the 15 best-ranked features displayed the best performance (AUC: 0.816). Prediction models using the five best PET-derived features outperformed those using five clinical variables.

CONCLUSION

The machine learning model using PET-derived radiomic features showed good performance for predicting the recurrence of NSCLC during the first 2 years after a curative intent therapy. PET/CT-based radiomic features may help clinicians improve the risk stratification of relapsed NSCLC.

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Objectives

Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).

Materials and methods

A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.

Results

Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.

Conclusion

AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

Lung squamous cell carcinoma presenting as rare clustered cystic lesions: A case report and review of literature

BACKGROUND

Lung cancer is the leading cause of cancer-related death. Early diagnosis is critical to improving a patient’s chance of survival. However, lung cancer associated with cystic airspaces is often misdiagnosed or underdiagnosed due to the absence of clinical symptoms, poor imaging specificity, and high risk of biopsy-related complications.

CASE SUMMARY

We report an unusual case of cancer in a 55-year-old man, in which the lesion evolved from a small solitary thin-walled cyst to lung squamous cell carcinoma (SCC) with metastases in both lungs. The SCC manifested as rare clustered cystic lesions, detected on chest computed tomography. There were air-fluid levels, compartments, and bronchial arteries in the cystic lesions. Additionally, there was no clear extrathoracic metastasis. After chemotherapy, the patient achieved a partial response, type I respiratory failure was relieved, and the lung lesions became a clustered thin-walled cyst.

CONCLUSION

Pulmonary cystic lesions require regular imaging follow-up. Lung SCC should be a diagnostic consideration in cases of thin-walled cysts as well as multiple clustered cystic lesions.

The Multidisciplinary Approach in Stage III Non-Small Cell Lung Cancer over Ten Years: From Radiation Therapy Optimisation to Innovative Systemic Treatments

Background: About 30% of new non-small cell lung cancer (NSCLC) cases are diagnosed at a locally advanced stage, which includes a highly heterogeneous group of patients with a wide spectrum of treatment options. The management of stage III NSCLC involves a multidisciplinary team, adequate staging, and a careful patient selection for surgery or radiation therapy integrated with systemic treatment. Methods: This is a single-center observational retrospective and prospective study including a consecutive series of stage III NSCLC patients who were referred to the Veneto Institute of Oncology and University Hospital of Padova (Italy) between 2012 and 2021. We described clinico-pathological characteristics, therapeutic pathways, and treatment responses in terms of radiological response in the entire study population and in terms of pathological response in patients who underwent surgery after induction therapy. Furthermore, we analysed survival outcomes in terms of relapse-free survival (RFS) and overall survival (OS). Results: A total of 301 patients were included. The majority of patients received surgical multimodality treatment (n = 223, 74.1%), while the remaining patients (n = 78, 25.9%) underwent definitive CRT followed or not by durvalumab as consolidation therapy. At data cut-off, 188 patients (62.5%) relapsed and the median RFS (mRFS) of the entire population was 18.2 months (95% CI: 15.83−20.57). At the time of analyses 140 patients (46.5%) were alive and the median OS (mOS) was 44.7 months (95% CI: 38.4−51.0). A statistically significant difference both in mRFS (p = 0.002) and in mOS (p < 0.001) was observed according to the therapeutic pathway in the entire population, and selecting patients treated after 2018, a significant difference in mRFS (p = 0.006) and mOS (p < 0.001) was observed according to treatment modality. Furthermore, considering only patients diagnosed with stage IIIB-C (N = 131, 43.5%), there were significant differences both in mRFS (p = 0.047) and in mOS (p = 0.022) as per the treatment algorithm. The mRFS of the unresectable population was 16.3 months (95% CI: 11.48−21.12), with a significant difference among subgroups (p = 0.030) in favour of patients who underwent the PACIFIC-regimen; while the mOS was 46.5 months (95% CI: 26.46−66.65), with a significant difference between two subgroups (p = 0.003) in favour of consolidation immunotherapy. Conclusions: Our work provides insights into the management and the survival outcomes of stage III NSCLC over about 10 years. We found that the choice of radical treatment impacts on outcome, thus suggesting the importance of appropriate staging at diagnosis, patient selection, and of the multidisciplinary approach in the decision-making process. Our results confirmed that the PACIFIC trial and the following introduction of durvalumab as consolidation treatment may be considered as a turning point for several improvements in the diagnostic-therapeutic pathway of stage III NSCLC patients.