Diagnosis of visceral pleural invasion using confocal laser endomicroscopy during lung cancer surgery

Advances in early detection of non-small cell lung cancer: A comprehensive review

BACKGROUND

Lung cancer has the highest mortality rate among malignancies globally. In addition, due to the growing number of smokers there is considerable concern over its growth. Early detection is an essential step towards reducing complications in this regard and helps to ensure the most effective treatment, reduce health care costs, and increase survival rates.

AIMS

To define the most efficient and cost-effective method of early detection in clinical practice.

MATERIALS AND METHODS

We collected the Information used to write this review by searching papers through PUBMED that were published from 2021 to 2024, mainly systematic reviews, meta-analyses and clinical-trials. We also included other older but notable papers that we found essential and valuable for understanding.

RESULTS

EB-OCT has a varied sensitivity and specificity-an average of 94.3% and 89.9 for each. On the other hand, detecting biomarkers via liquid biopsy carries an average sensitivity of 91.4% for RNA molecules detection, and 97% for combined methylated DNA panels. Moreover, CTCs detection did not prove to have a significant role as a screening method due to the rarity of CTCs in the bloodstream thus the need for more blood samples and for enrichment techniques.

DISCUSSION

Although low-dose CT scan (LDCT) is the current golden standard screening procedure, it is accompanied by a highly false positive rate. In comparison to other radiological screening methods, Endobronchial optical coherence tomography (EB-OCT) has shown a noticeable advantage with a significant degree of accuracy in distinguishing between subtypes of non-small cell lung cancer. Moreover, numerous biomarkers, including RNA molecules, circulating tumor cells, CTCs, and methylated DNA, have been studied in the literature. Many of these biomarkers have a specific high sensitivity and specificity, making them potential candidates for future early detection approaches.

CONCLUSION

LDCT is still the golden standard and the only recommended screening procedure for its high sensitivity and specificity and proven cost-effectiveness. Nevertheless, the notable false positive results acquired during the LDCT examination caused a presumed concern, which drives researchers to investigate better screening procedures and approaches, particularly with the rise of the AI era or by combining two methods in a well-studied screening program like LDCT and liquid biopsy. we suggest conducting more clinical studies on larger populations with a clear demographical target and adopting approaches for combining one of these new methods with LDCT to decrease false-positive cases in early detection.

Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning

PURPOSE

To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.

METHODS

Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients.

RESULTS

The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons.

CONCLUSION

The deep learning model systems can be utilized in clinical applications via data expansion.

[Probe based confocal laser endomicroscopy in thoracic endoscopy]

INTRODUCTION

Probe based confocal laser endomicroscopy (pCLE) is a new endoscopic imaging technology. It uses mini probes which can be introduced through the working channels of endoscopes. Whenever applied on the tissue of interest, they allow imaging of tissue at a cellular level.

STATE OF ART

In the filed of pleuropulmonary malignancies, pCLE showed mostly its ability to guide biopsies samplings. Those results need to be validated in larger prospective studies. In interstitial lung diseases, pCLE provides information complementary to other clinical and paraclinical data. The valuability of these informations need to be investigated further, prospectively in randomized trials. In obstructive pulmonary diseases, pCLE is able to investigate the structural and functional relationships between pulmonary structures. pCLE showed good ability in the identification of acute cellular rejection after lung transplantation.

PERSPECTIVES AND CONCLUSION

For the time being, pCLE is not part of routine clinical practice. The data available need to be validated in larger randomized prospective trials, before it can be recommended as a guiding tool for biopsies or as a diagnostic tool for pathologic process. New fluorophores are now available. They are specific of some molecular sequences, allowing the enhancement of specific targets within the sample studied.

Research progress in predicting visceral pleural invasion of lung cancer: a narrative review

Background and Objective

In lung cancer, visceral pleural invasion (VPI) affects the selection of surgical methods, the scope of lymph node dissection and the need for adjuvant chemotherapy. Preoperative or intraoperative prediction and diagnosis of VPI of lung cancer is helpful for choosing the best treatment plan and improving the prognosis of patients. This review aims to summarize the research progress of the clinical significance of VPI assessment, the intraoperative diagnosis technology of VPI, and various imaging methods for preoperative prediction of VPI. The diagnostic efficacy, advantages and disadvantages of various methods were summarized. The challenges and prospects for future research will also be discussed.

Methods

A comprehensive, non-systematic review of the latest literature was carried out in order to investigate the progress of predicting VPI. PubMed database was being examined and the last run was on 4 August 2022.

Key Content and Findings

The pathological diagnosis and clinical significance of VPI of lung cancer were discussed in this review. The research progress of prediction and diagnosis of VPI in recent years was summarized. The results showed that preoperative imaging examination and intraoperative freezing pathology were of great value.

Conclusions

VPI is one of the adverse prognostic factors in patients with lung cancer. Accurate prediction of VPI status before surgery can provide guidance and help for the selection of clinical operation and postoperative treatment. There are some advantages and limitations in predicting VPI based on traditional computed tomography (CT) signs, 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT and magnetic resonance imaging (MRI) techniques. As an emerging technology, radiomics and deep learning show great potential and represent the future research direction.

Selective Mediastinal Lymph Node Dissection Strategy for Clinical T1N0 Invasive Lung Cancer: A Prospective, Multicenter, Clinical Trial

INTRODUCTION

We aimed to prospectively evaluate our previously proposed selective mediastinal lymph node (LN) dissection strategy for peripheral clinical T1N0 invasive NSCLC.

METHODS

This is a multicenter, prospective clinical trial in China. We set six criteria for predicting negative LN stations and finally guiding selective LN dissection. Consolidation tumor ratio less than or equal to 0.5, segment location, lepidic-predominant adenocarcinoma (LPA), negative hilar nodes (stations 10-12), and negative visceral pleural invasion (VPI) were used separately or in combination as predictors of negative LN status in the whole, superior, or inferior mediastinal zone. LPA, hilar node involvement, and VPI were diagnosed intraoperatively. All patients actually underwent systematic mediastinal LN dissection. The primary end point was the accuracy of the strategy in predicting LN involvement. If LN metastasis occurred in certain mediastinal zone that was predicted to be negative, it was considered as an "inaccurate" case.

RESULTS

A total of 720 patients were enrolled. The median number of LN dissected was 15 (interquartile range: 11-20). All negative node status in certain mediastinal zone was correctly predicted by the strategy. Compared with final pathologic findings, the accuracy of frozen section to diagnose LPA, VPI, and hilar node metastasis was 94.0%, 98.9%, and 99.6%, respectively. Inaccurate intraoperative diagnosis of LPA, VPI, or hilar node metastasis did not lead to inaccurate prediction of node-negative status.

CONCLUSIONS

This is the first prospective trial validating the specific mediastinal LN metastasis pattern in cT1N0 invasive NSCLC, which provides important evidence for clinical applications of selective LN dissection strategy.

The role of confocal laser endomicroscopy in pulmonary medicine

Accurate diagnosis and subsequent therapeutic options in pulmonary diseases mainly rely on imaging methods and histological assessment. However, imaging examinations are hampered by the limited spatial resolution of images and most procedures that are related to histological assessment are invasive with associated complications. As a result, a high-resolution imaging technology - confocal laser endomicroscopy (CLE), which is at the forefront and enables real-time microscopic visualisation of the morphologies and architectures of tissues or cells - has been developed to resolve the clinical dilemma pertaining to current techniques. The current evidence has shown that CLE has the potential to facilitate advanced diagnostic capabilities, to monitor and to aid the tailored treatment regime for patients with pulmonary diseases, as well as to expand the horizon for unravelling the mechanism and therapeutic targets of pulmonary diseases. In the future, if CLE can be combined with artificial intelligence, early, rapid and accurate diagnosis will be achieved through identifying the images automatically. As promising as this technique may be, further investigations are required before it can enter routine clinical practice.