Imaging patterns of early stage lung cancer for the thoracic surgeon

Palmucci, Stefano; Nardini, Marco; Basile, Antonio

doi:10.21037/jtd.2020.02.61

Cited by 5 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the possible advantages of lung cancer screening have to be balanced against the potential of inducing harm, over-diagnosing, cost-effectiveness, or the incapability of early diagnosis [5]. Despite the fact that lung cancer screening with computed tomography has been proven to substantially decrease lung cancer specific mortality, its application is challenging in multiple aspects such as variability in radiological standards, the pre-selection of at-risk populations, false positive rates, and over-diagnosing [5][6][7][8]. Therefore, new methods for simple, accurate, and non-invasive diagnosis of lung cancer, especially in curative stages, would be highly desirable.…”

Section: Introductionmentioning

confidence: 99%

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Herth,

Schmidt,

Basler

et al. 2024

J. Breath Res.

View full text Add to dashboard Cite

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient's breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2 fold change ≥ 1 and q-values ≥ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z -242.18428 and m/z -117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Herth,

Schmidt,

Basler

et al. 2024

J. Breath Res.

View full text Add to dashboard Cite

show abstract

“…VATS has improved the long-term survival rate of lung cancer and provides a minimally invasive approach for the removal of pulmonary nodules [2]. However, early lung adenocarcinomas are difficult to palpate during VATS and unfeasible to pathologically evaluate from fine needle aspiration or biopsy [3]. Therefore, preoperative and intraoperative localization, using materials such as microcoils, hookwires, contrast media, dyes, and fluorescence tracers, is important for thoracic surgeons [4].…”

Section: Introductionmentioning

confidence: 99%

Pulmonary Recruitment Prior to Intraoperative Multiple Pulmonary Ground-Glass Nodule Localization Increases the Localization Accuracy—A Retrospective Study

Wang

Huang

et al. 2023

JCM

View full text Add to dashboard Cite

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Preoperative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve the respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase the localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared the localization accuracy between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. The patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

show abstract

“…The imaging features of lung cancer vary from a single tiny nodule to ground-glass opacity, multiple nodules, pleural effusion, lung collapse, and multiple opacities [4]; simple and small lesions are extremely difficult to detect [5]. Histopathological features include adenocarcinoma, squamous cell carcinoma, small cell carcinoma, and many other rare histological types.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Lung Cancer

Chiu

Chao

Chen

2022

Cancers

View full text Add to dashboard Cite

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

show abstract

Imaging patterns of early stage lung cancer for the thoracic surgeon

Cited by 5 publications

References 37 publications

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Pulmonary Recruitment Prior to Intraoperative Multiple Pulmonary Ground-Glass Nodule Localization Increases the Localization Accuracy—A Retrospective Study

Application of Artificial Intelligence in Lung Cancer

Contact Info

Product

Resources

About