Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions

New insights into imaging of pulmonary metastases from extra-thoracic neoplasms

The lung is one of the most common sites of metastases from extra-thoracic neoplasms. Lung metastases can show heterogeneous imaging appearance, thus mimicking a wide range of lung diseases, from benign lesions to primary lung cancer. The proper interpretation of pulmonary findings is crucial for prognostic assessment and treatment planning, even to avoid unnecessary procedures and patient anxiety. For this purpose, computed tomography (CT) is one of the most used imaging modalities. In the last decades, cancer patients' population has steadily increased and, due to the widespread application of CT for staging and surveillance, the detection of pulmonary nodules has raised, making their characterization and management an urgent and mostly unsolved problem for both radiologists and clinicians. This review will highlight the pathways of dissemination of extra-thoracic tumours to the lungs and the heterogeneous CT imaging appearance of pulmonary metastases, providing useful clues to properly address the diagnosis. Furthermore, we will deal with the promising applications of radiomics in this field. Finally, a focus on the hot-topic of pulmonary nodule management in patients with extra-thoracic neoplasms (ETNs) will be discussed.

Do resected colorectal cancer patients need early chest imaging? Impact of clinicopathologic characteristics on time to development of pulmonary metastases

BACKGROUND AND OBJECTIVES

For patients with colorectal cancer (CRC), the lung is the most common extra-abdominal site of distant metastasis. However, practices for chest imaging after colorectal resection vary widely. We aimed to identify characteristics that may indicate a need for early follow-up imaging.

METHODS

We retrospectively reviewed charts of patients who underwent CRC resection, collecting clinicopathologic details and oncologic outcomes. Patients were grouped by timing of pulmonary metastases (PM) development. Analyses were performed to investigate odds ratio (OR) of PM diagnosis within 3 months of CRC resection.

RESULTS

Of 1600 patients with resected CRC, 233 (14.6%) developed PM, at a median of 15.4 months following CRC resection. Univariable analyses revealed age, receipt of systemic therapy, lymph node ratio (LNR), lymphovascular and perineural invasion, and KRAS mutation as risk factors for PM. Furthermore, multivariable regression showed neoadjuvant therapy (OR: 2.99, p < 0.001), adjuvant therapy (OR: 6.28, p < 0.001), LNR (OR: 28.91, p < 0.001), and KRAS alteration (OR: 5.19, p < 0.001) to predict PM within 3 months post-resection.

CONCLUSIONS

We identified clinicopathologic characteristics that predict development of PM within 3 months after primary CRC resection. Early surveillance in such patients should be emphasized to ensure timely identification and treatment of PM.

Radiomics in Lung Metastases: A Systematic Review

Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.

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.

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

Purpose

Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.

Methods

This retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.

Results

Five clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical-radiographic model (P=0.049).

Conclusions

The proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

V7 ENB-guided thoracoscopic sublobectomy for stage IA synchronous multiple primary lung cancer

BACKGROUND

An increasing number of patients are being diagnosed with synchronous multiple primary lung cancer (SMPLC) with the popularization of lung cancer screening programs. However, a strategy for accurate location and suitable surgery therapy is still lacking. The present study aimed to explore the accuracy and feasibility of electromagnetic navigation bronchoscopy (ENB)-guided thoracoscopic sublobectomy for stage IA SMPLC.

METHODS

Patients with SMPLC who underwent ENB-guided sublobectomy from January 2020 to June 2022 were enrolled in this study. The analysis of localization accuracy of ENB and surgical outcome was conducted.

RESULTS

Overall, 138 patients with 353 malignant nodules were enrolled. The tumor size was 0.7 cm (range from 0.5 to 1.1 cm). ENB localization was performed on 162 nodules, and a customized scoring system was developed to evaluate localization accuracy. The success rate of localization was 98.3% (178/181). Notably, localization accuracy was positively correlated with bronchial signs (p < 0.01) and negatively correlated with the distance from the nodule to the pleura (p = 0.02). All nodules were completely resected. Operation time, drainage volume on the third postoperative day, and catheter time were significantly correlated with the resected lesion numbers (p = 0.009, p = 0.004, and p = 0.01, respectively).

CONCLUSIONS

ENB-guided uniportal video-assisted thoracoscopic sublobectomy provides accurate preoperative localization and avoids unnecessary lung resection of patients with stage IA SMPLC. However, complete resection of multilocation nodules (more than four lesions) increases the risk of postoperative complications. A new combined treatment strategy for SMPLC should be explored.