Recurrence and Survival After Segmentectomy in Patients With Prior Lung Resection for Early-Stage Non-Small Cell Lung Cancer

Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing

PURPOSE

Recurrences after curative resection in early-stage and locoregionally advanced non-small cell lung cancer (NSCLC) are common, necessitating a nuanced understanding of associated risk factors. This study aimed to establish a natural language processing (NLP) system to efficiently curate recurrence data in NSCLC and analyze risk factors longitudinally.

PATIENTS AND METHODS

Electronic health records of 6,351 patients with NSCLC with >700,000 notes were obtained from Mount Sinai's data sets. A deep learning-based customized NLP system was developed to identify cohorts experiencing recurrence. Recurrence types and rates over time were stratified by various clinical features. Cohort description analysis, Kaplan-Meier analysis for overall recurrence-free survival (RFS) and distant metastasis-free survival (DMFS), and Cox proportional hazards analysis were performed.

RESULTS

Of 1,295 patients with stage I-IIIA NSCLC with surgical resections, 336 patients (25.9%) experienced recurrence, as identified through NLP. The NLP system achieved a precision of 94.3%, a recall of 93%, and an F1 score of 93.5. Among 336 patients, 52.4% had local/regional recurrences, 44% distant metastases, and 3.6% unknown recurrence. RFS rates at years 1-5 were 93%, 81%, 73%, 67%, and 61%, respectively (96%, 89%, 84%, 80%, and 75% for distant metastasis). Stage-specific RFS rates at year 5 were 73% (IA), 62% (IB), 47% (IIA), 46% (IIB), and 20% (IIIA). Stage IB patients had a significantly higher likelihood of recurrence versus stage IA (adjusted hazard ratio [aHR], 1.63; P = .02). The RFS was lower in patients with clinically significant TP53 alteration (v TP53-negative or unknown significance), affecting overall RFS (aHR, 1.89; P = .007) and DMFS (aHR, 2.47; P = .009) among stage IA/IB patients.

CONCLUSION

Our scalable NLP system enabled us to generate real-world insights into NSCLC recurrences, paving the way for predictive models for preventing, diagnosing, and treating NSCLC recurrence.

STAS: New explorations and challenges for thoracic surgeons

Spread through air spaces (STAS) represents a relatively novel concept in the pathology of lung cancer, and it specifically refers to the dissemination of tumour cells into the parenchymal air spaces adjacent to the primary tumour. In 2015, the World Health Organization (WHO) classified STAS as a new invasive form of lung adenocarcinoma (LUAD). Many studies investigated the role of STAS and revealed its association with the prognosis of LUAD and its influence on the outcomes of other malignant pulmonary neoplasms. Additionally, the underlying mechanisms and predictive models of STAS have received considerable attention in recent years. This paper provides a comprehensive overview of the research advancements and prospects of STAS by examining it from multiple perspectives.

Impact of Margin Distance on Locoregional Recurrence and Survival After Thoracoscopic Segmentectomy

BACKGROUND

This study aimed to identify the impact of margin distance on locoregional recurrence (LRR) and survival outcomes after thoracoscopic segmentectomy for non-small cell lung cancer.

METHODS

We retrospectively analyzed data from prospectively collected consecutive thoracoscopic segmentectomies in a single center from January 2008 to February 2023. The restricted cubic spline of the adjusted Cox regression model for LRR displayed the breakpoint of margin distance. The Kaplan-Meier estimator with log-rank test evaluated the overall survival between the 2 groups stratified by the breakpoint, and the Aalen-Johansen estimator with the Gray test assessed the LRR-free survival and lung cancer-specific survival in the competing model.

RESULTS

The study included 155 patients. LRR was observed in 22 patients (14.2%), with a median time to LRR of 17.1 months (interquartile range, 6.3-26.3 months). Margin distance was found to be a predictor for LRR (hazard ratio, 0.92; P = .033). The identified breakpoint for margin distance in this cohort was 19.8 mm. Compared with this cutoff, a margin distance of 15 mm increased the risk of LRR by 65%, whereas 25 mm decreased the risk to LRR with 31%. A segmentectomy with a margin distance ≥20 mm resulted in significant improvements in overall survival (P = .020), lung cancer-specific survival (P = .010), and LRR-free survival (P < .001) compared with cases with a margin distance of <20 mm.

CONCLUSIONS

Margin distance ≥20 mm decreased LRR and improved survival outcomes for thoracoscopic segmentectomy in this study.

Advancing presurgical non-invasive spread through air spaces prediction in clinical stage IA lung adenocarcinoma using artificial intelligence and CT signatures

Background

To accurately identify spread through air spaces (STAS) in clinical stage IA lung adenocarcinoma, our study developed a non-invasive and interpretable biomarker combining clinical and radiomics features using preoperative CT.

Methods

The study included a cohort of 1,325 lung adenocarcinoma patients from three centers, which was divided into four groups: a training cohort (n = 930), a testing cohort (n = 238), an external validation 1 cohort (n = 93), and 2 cohort (n = 64). We collected clinical characteristics and semantic features, and extracted radiomics features. We utilized the LightGBM algorithm to construct prediction models using the selected features. Quantifying the contribution of radiomics features of CT to prediction model using Shapley additive explanations (SHAP) method. The models' performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), negative predictive value (NPV), positive predictive value (PPV), sensitivity, specificity, calibration curve, and decision curve analysis (DCA).

Results

In the training cohort, the clinical model achieved an AUC value of 0.775, the radiomics model achieved an AUC value of 0.836, and the combined model achieved an AUC value of 0.837. In the testing cohort, the AUC values of the models were 0.743, 0.755, and 0.768. In the external validation 1 cohort, the AUC values of the models were 0.717, 0.758, and 0.765, while in the external validation 2 cohort, 0.725, 0.726 and 0.746. The DeLong test results indicated that the combined model outperformed the clinical model (p < 0.05). DCA indicated that the models provided a net benefit in predicting STAS. The SHAP algorithm explains the contribution of each feature in the model, visually demonstrating the impact of each feature on the model's decisions.

Conclusion

The combined model has the potential to serve as a biomarker for predicting STAS using preoperative CT scans, determining the appropriate surgical strategy, and guiding the extent of resection.

Collagen 1 Fiber Volume Predicts for Recurrence of Stage 1 Non-Small Cell Lung Cancer

Background: The standard of care for stage 1 NSCLC is upfront surgery followed by surveillance. However, 20-30% of stage 1 NSCLC recur. There is an unmet need to identify individuals likely to recur who would benefit from frequent monitoring and aggressive cancer treatments. Collagen 1 (Col1) fibers detected by second harmonic generation (SHG) microscopy are a major structural component of the extracellular matrix (ECM) of tumors that play a role in cancer progression. Method: We characterized Col1 fibers with SHG microscopy imaging of surgically resected stage 1 NSCLC. Gene expression from RNA sequencing data was used to validate the SHG microscopy findings. Results: We identified a significant (p ≤ 0.05) increase in the Col1 fiber volume in stage 1 NSCLC that recurred. The increase in Col1 fiber volume was supported by significant increases in the gene expression of Col1 in invasive, compared to noninvasive, lung adenocarcinoma. Significant differences were identified in the gene expression of other ECM proteins, as well as CAFs, immune checkpoint markers, immune cytokines, and T-cell markers. Conclusion: Col1 fiber analysis can provide a companion diagnostic test to evaluate the likelihood of tumor recurrence following stage 1 NSCLC. The studies expand our understanding of the role of the ECM in NSCLC recurrence.

Molecular characteristics of multiple primary pulmonary nodules under a three-dimensional reconstruction model and relevant multi-omics analyses: a case report

Background

In addition to CT images and pathological features, many other molecular characteristics remain unknown about multiple primary lung cancer (MPLC) from intrapulmonary metastatic lung cancer.

Case presentation

In this study, we reported a patient with an early-stage MPLC with both adenocarcinoma in situ (AIS) subtype and minimally invasive adenocarcinoma (MIA) subtype. The patient was diagnosed with more than 10 nodules and underwent precise surgery assisted by three-dimensional (3D) reconstruction at the left upper lung lobe. Whole-exome sequencing (WES) and multiple immunohistochemistry (mIHC) were performed to reveal the genomic profiling and tumor microenvironments of multiple nodules in this patient with MPLC. Based on 3D reconstruction location information, we found that the genomic and pathological results of adjacent lymph nodes were quite different. On the other hand, PD-L1 expression and the proportion of infiltrating lymphocytes in tumor microenvironments were all at a low status and did not vary in adjacent lymph nodes. Additionally, maximum diameter and tumor mutational burden levels were found to be significantly associated with CD8+ T cell proportion (p<0.05). Besides, CD163+ macrophages and CD4+ T cell proportion were higher in MIA nodules than in AIS nodules (p<0.05). This patient reached a recurrence-free survival of 39 months.

Conclusion

Generally, in addition to CT imaging and pathological results, genomic profiling and tumor microenvironments may facilitate identifying the potential molecular mechanisms and clinical outcomes in patients with early-stage MPLC.

Intersegmental plane simulation based on the bronchus-vein-artery triad in pulmonary segmentectomy

Background

Few reliable methods to simulate and evaluate the intersegmental plane have been reported. We introduce intersegmental plane simulation based on the bronchus-vein-artery triad in three-dimensionally reconstructed images from patients who underwent segmentectomy for early lung cancer.

Methods

We collected clinical data of consecutive patients with early-stage lung cancer who underwent three-dimensional imaging-guided single-port thoracoscopic segmentectomy at Department No. 1 of Thoracic Surgery at Fujian Medical University Fujian Union Hospital from January 2019 to July 2019. Patients were divided into two groups according to the application of intersegmental plane simulation and nodule analysis: the intersegmental plane group and the non-intersegmental plane group. General clinical characteristics, operation status, and postoperative recovery were compared between groups. The three-dimensional reconstruction results in the intersegmental plane group were analyzed and summarized.

Results

A total of 120 patients were included (61 in the intersegmental plane group and 59 in the non-intersegmental plane group). There were no significant differences between the two groups in general characteristics (all P>0.05). All target lesions were resected in both groups. There were no significant differences between groups in operation characteristics or postoperative recovery, with the exception of the duration of chest drainage and the rate of gross margin insufficiency. There were five cases of gross margin insufficiency in the non-intersegmental plane group. With three-dimensional imaging reconstruction, a total of 131 intersegmental veins could be used to evaluate the simulated intersegmental plane in 61 patients, with an average of 2.1±0.5 veins per patient. Two patients (3.3%) had one vein that could be used to evaluate the intersegmental plane, 50 patients (82.3%) had two, seven patients (11.3%) had three, and two patients (3.3%) had four. The total number of intersegmental veins located on the simulated intersegmental plane was 124 (94.7%), with an average of 2.0±0.6 veins per patient. The accuracy of intersegmental plane simulation was 91.8% (56/61).

Conclusions

The bronchus-vein-artery triad in intersegmental plane simulation can assist surgeons in preoperative planning and can facilitate complete resection of early lung cancer with sufficient surgical margins.

Long-Term Outcomes in Stage I Lung Cancer After Segmentectomy with a Close Resection Margin

Background

In general, a 2-cm surgical margin is recommended for limited resection to obtain equivalent oncologic outcomes to lobectomy for lung cancer. This study aimed to examine the patterns of recurrence and prognostic factors for recurrence in patients with a close parenchymal resection margin.

Methods

From January 2009 to April 2017, 156 patients with stage I lung cancer who underwent segmentectomy with a close resection margin (<2 cm) were enrolled. Recurrence-free survival and overall survival were assessed. In addition, predisposing factors for recurrence were evaluated.

Results

The mean tumor size was 1.7±0.8 cm and the parenchymal resection margin was 1.1±0.6 cm. Recurrence developed in 17 (10.7%) of the 156 patients, and the 5-year recurrence-free survival rate was 88.9%. Distant metastasis (7.7%) was the predominant recurrence pattern. The isolated local recurrence rate was 1.9%. Multivariate Cox regression analysis revealed that age, tumor size, mediastinal lymph node dissection, postoperative complications, and histologic type were significant predisposing factors for recurrence. However, parenchymal margin distance did not significantly affect the long-term prognosis.

Conclusion

Segmentectomy with a close resection margin for early-stage lung cancer in selected patients resulted in acceptable recurrence and survival. However, patients with tumors larger than 2 cm, squamous cell carcinoma histology, and insufficient mediastinal evaluation should be carefully followed up for recurrence.

Roles of Lipid Profiles in Human Non-Small Cell Lung Cancer

Aims: This review aims to identify lipid biomarkers of non-small cell lung cancer (NSCLC) in human tissue samples and discuss the roles of lipids in tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment. Methods: A review of the literature focused on lipid-related research using mass spectrometry (MS) techniques in human NSCLC tissues from January 1, 2015, to November 20, 2020, was conducted. The quality of included studies was assessed using the QUADAS-2 tool. Results: Twelve studies met the inclusion criteria and were included in the review. The risk of bias was unclear in the majority of the studies. The contents of lipids including fatty acids, phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl inositol, cardiolipin, phosphatidyl serine, phosphatidyl glycerol, ceramide, lysophosphatidylethanolamine, lysophosphatidylcholine, and lysophosphatidylglycerol differed significantly between cancer and healthy tissues. The sensitivity or specificity of the discrimination model was reported in 8 studies, and the sensitivity and specificity varied among the reported methods. The lipid profiles differed between adenocarcinoma and squamous cell carcinoma NSCLC subtypes. Conclusion: In preclinical studies, MS analysis and multiple discrimination models can be combined to distinguish NSCLC tissues from healthy tissues based on lipid profiles, which provides a new opportunity to evaluate the surgical margin and cancer subtype intraoperatively. Future studies should provide guidance for selecting patients and discrimination models to develop an improved method for clinical application.

Accuracy and Reproducibility of Intraoperative Assessment on Tumor Spread Through Air Spaces in Stage 1 Lung Adenocarcinomas

INTRODUCTION

Tumor spread through air spaces (STAS) is associated with worse prognosis in early-stage lung adenocarcinomas, particularly in sublobar resection. Intraoperative consultation for STAS has been advocated to guide surgical management. However, data on accuracy and reproducibility of intraoperative assessment of STAS remain limited. We evaluated diagnostic yield, interobserver agreement (IOA), and intraobserver agreement (ITA) for STAS detection on frozen section (FS).

METHODS

A panel of three pathologists evaluated stage 1 lung adenocarcinomas (n = 100) for the presence or absence of STAS and artifacts as reference. Five pulmonary pathologists independently reviewed all cases in two rounds, detecting STAS and artifacts in FS and the corresponding FS permanent and non-FS permanent, with a consensus conference between rounds.

RESULTS

The FS had low sensitivity (44%), high specificity (91%), relatively high accuracy (71%), and overall area under the receiver operating characteristic curve of 0.67 for detecting STAS. The average ITA was moderate for both STAS (κ_mean: 0.598) and artifact (κ_mean: 0.402) detection on FS. IOA was moderate for STAS (κ_round-1: 0.453; κ_round-2: 0.506) and fair for artifact (κ_round-1: 0.300; κ_round-2: 0.204) detection on FS. IOA for STAS improved in FS permanent and non-FS permanent, whereas ITA was similar across section types. On multivariable logistic regression, the only significant predictor of diagnostic discordance was the presence of artifacts.

CONCLUSIONS

FS is highly specific but not sensitive for STAS detection in stage 1 lung adenocarcinomas. IOA on STAS is moderate in FS and improved only marginally after a consensus conference, raising concerns regarding global implementation of intraoperative assessment of STAS and warranting more precise criteria for STAS and artifacts.

Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs

OBJECTIVES

To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer.

METHODS

In this retrospective study, dataset 1 (for training, tuning, and internal validation) included 676 patients with clinical stage IA lung adenocarcinomas resected between 2009 and 2015. Dataset 2 (for temporal validation) included 141 patients with clinical stage I adenocarcinomas resected between 2017 and 2018. A CT-based deep learning model was developed for the prediction of VPI and validated in terms of discrimination and calibration. An observer performance study and a multivariable regression analysis were performed.

RESULTS

The area under the receiver operating characteristic curve (AUC) of the model was 0.75 (95% CI, 0.67-0.84), which was comparable to those of board-certified radiologists (AUC, 0.73-0.79; all p > 0.05). The model had a higher standardized partial AUC for a specificity range of 90 to 100% than the radiologists (all p < 0.05). The high sensitivity cutoff (0.245) yielded a sensitivity of 93.8% and a specificity of 31.2%, and the high specificity cutoff (0.448) resulted in a sensitivity of 47.9% and a specificity of 86.0%. Two of the three radiologists provided highly sensitive (93.8% and 97.9%) but not specific (48.4% and 40.9%) diagnoses. The model showed good calibration (p > 0.05), and its output was an independent predictor for VPI (adjusted odds ratio, 1.07; 95% CI, 1.03-1.11; p < 0.001).

CONCLUSIONS

The deep learning model demonstrated a radiologist-level performance. The model could achieve either highly sensitive or highly specific diagnoses depending on clinical needs.

KEY POINTS

• The preoperative CT-based deep learning model demonstrated an expert-level diagnostic performance for the presence of visceral pleural invasion in early-stage lung cancer. • Radiologists had a tendency toward highly sensitive, but not specific diagnoses for the visceral pleural invasion.

Novel imprint cytological classification is correlated with tumor spread through air spaces in lung adenocarcinoma

OBJECTIVES

Spread through air spaces (STAS) is a risk factor for local recurrence after sublobar resection in lung cancer patients. We recently proposed the novel Nakayama-Higashiyama imprint cytological classification (N-H classification) based on small-sized lung adenocarcinoma surgical specimens, which correlated with histological patterns and nodal involvement. This study aimed to evaluate the correlation between STAS and the N-H classification and to validate the N-H classification as an intraoperative predictor of the presence of STAS.

MATERIALS AND METHODS

We retrospectively analyzed 164 intraoperative imprint cytologies and their paired histologic specimens from patients undergoing surgical resection for lung adenocarcinoma in our institute in 2017-2019. Using the NH classification, imprint cytological findings were classified into 5 groups (Groups I to V) based on cell cluster shape, cell and nucleus size, and the existence of necrosis. We examined the characteristics of imprint cytology and STAS in the resected tissues and analyzed the relationship between them.

RESULTS

Tumor STAS was observed in 29 (17.7 %) cases. The presence of STAS was significantly associated with the NH classification (P < 0.0001). STAS was present in 6 of 57 cases (10.5 %) in NH classification Group II, 11 of 42 cases (26.2 %) in Group III, and 12 of 28 cases (42.9 %) in Group IV/V; STAS was not observed in any case in Group I. Logistic regression analysis revealed that tumors with a ground glass opacity rate of <50 % on computed tomography (P = 0.00867) and Groups III-V of the NH classification (P = 0.00201) were significant independent predictors for STAS.

CONCLUSION

Intraoperative imprint cytology with the N-H classification for lung adenocarcinoma is well correlated with the STAS status of the tumor and might have applications as an intraoperative predictive marker of STAS. This classification may be useful for intraoperative detection of STAS and in the decision-making process for the surgical procedure.

Significance of tumor spread through air spaces (STAS) in lung cancer from the pathologist perspective

Airspace invasion in lung cancer has been known over the last 30 years, but it was only recently that WHO 2015 formally recognized it as a mechanism of invasion with the terminology of tumor spread through air spaces (STAS). Multiple studies have shown the association of STAS with lower survival and suggest that STAS is an independent prognostic factor across lung adenocarcinoma of all stages and in other histologic subtypes of lung cancer as well. Consequently, STAS is designated as an exclusion criterion of adenocarcinoma in situ and minimally invasive adenocarcinoma; thus, the presence of STAS impacts the diagnosis and staging of lung adenocarcinoma. Further, wedge resection and segmentectomy have been increasingly applied for small node negative tumors and the presence of STAS in those specimens may indicate the requirement of completion lobectomy. Given these significant clinical implications, we, pathologists, need to recognize and appropriately report STAS (possibly including at the time of intraoperative consultation). However, emerging data suggests that more work should be done to improve consensus and identification of STAS, including at frozen section. In this review, the evolution of our understanding of airspace invasion over the past decade, the clinical significance of STAS, and controversies and practical issues associated with the diagnosis of STAS are discussed.