Mediastinal staging for non-small cell lung cancer

Sublobar Resection of Non-Small-Cell Lung Cancer: Wedge Resection vs. Segmentectomy

Lung cancer is the most common cause of cancer death. The mainstay treatment for non-small-cell lung cancer (NSCLC), particularly in the early stages, is surgical resection. Traditionally, lobectomy has been considered the gold-standard technique. Sublobar resection includes segmentectomy and wedge resection. Compared to lobectomy, these procedures have been viewed as a compromise procedure, reserved for those with poor cardiopulmonary function or who are poor surgical candidates for other reasons. However, with the advances in imaging and surgical techniques, the subject of sublobar resection as a curative treatment is being revisited. Many studies have now shown segmentectomy to be equivalent to lobectomy in patients with small (<2.0 cm), peripheral NSCLC. However, there is a mix of evidence when it comes to wedge resection and its suitability as a curative procedure. At this time, until more data can be found, segmentectomy should be considered before wedge resection for patients with early-stage NSCLC.

A deep learning-based system for mediastinum station localization in linear EUS (with video)

Background and Objectives

EUS is a crucial diagnostic and therapeutic method for many anatomical regions, especially in the evaluation of mediastinal diseases and related pathologies. Rapidly finding the standard stations is the key to achieving efficient and complete mediastinal EUS imaging. However, it requires substantial technical skills and extensive knowledge of mediastinal anatomy. We constructed a system, named EUS-MPS (EUS-mediastinal position system), for real-time mediastinal EUS station recognition.

Methods

The standard scanning of mediastinum EUS was divided into 7 stations. There were 33 010 images in mediastinum EUS examination collected to construct a station classification model. Then, we used 151 videos clips for video validation and used 1212 EUS images from 2 other hospitals for external validation. An independent data set containing 230 EUS images was applied for the man-machine contest. We conducted a crossover study to evaluate the effectiveness of this system in reducing the difficulty of mediastinal ultrasound image interpretation.

Results

For station classification, the model achieved an accuracy of 90.49% in image validation and 83.80% in video validation. At external validation, the models achieved 89.85% accuracy. In the man-machine contest, the model achieved an accuracy of 84.78%, which was comparable to that of expert (83.91%). The accuracy of the trainees' station recognition was significantly improved in the crossover study, with an increase of 13.26% (95% confidence interval, 11.04%-15.48%; P < 0.05).

Conclusions

This deep learning-based system shows great performance in mediastinum station localization, having the potential to play an important role in shortening the learning curve and establishing standard mediastinal scanning in the future.

The impact of staging FDG-PET/CT on treatment for stage III NSCLC - an analysis of population-based data from Ontario, Canada

Purpose

Fluoro-2-deoxyglucose positron-emission tomography (FDG-PET/CT) is now considered a standard investigation for the staging of new cases of stage III NSCLC. However, there is not published level 3 evidence demonstrating the impact of FDG-PET/CT on appropriate therapy in this setting. Using retrospective population-based data, we sought to examine the role and timing that FDG-PET/CT scans play in influencing treatment choice, as well as survival in patients diagnosed with stage III NSCLC.

Materials and methods

A retrospective cohort of patients diagnosed with stage III NSCLC from 2009-2017 in Ontario were identified from the IC/ES (formerly Institute of Clinical Evaluative Sciences) database. FDG-PET/CT utilization over time, trends in mediastinal biopsy technique and usage, the impact of FDG-PET/CT on overall survival (OS), and its influence on use of concurrent chemoradiotherapy (CRT) were explored. The impact of timing of pre-treatment FDG-PET/CT on OS was also analyzed (≤28 days prior to treatment, 29-56 days prior, and >56 days prior).

Results

Between 2007 and 2017, a total of 13 796 people were diagnosed with stage III NSCLC in Ontario. FDG-PET/CT utilization increased over time with 0% of cases in 2007 and 74% in 2017 with pre-treatment FDG-PET/CT scans. The number of patients who received a mediastinal biopsy similarly increased in this timeframe increasing from 41% to 53%. More patients with pre-treatment FDG-PET/CT scans received curative-intent therapy than those who did not: 23% vs 13% for CRT (p<0.001), and 23% vs 10% for surgery (p<0.001). Median OS was longer in those with FDG-PET/CT scans prior to treatment (17 vs 11 months), as was 5-year survival (22% vs 14%, p<0.001), and this held true on both univariate and multivariate analyses. Timing of FDG-PET/CT scan relative to treatment was not associated with differences in OS.

Conclusion

Improvements in OS were seen in this cohort of stage III NSCLC patients who underwent a pre-treatment FDG-PET/CT scan. This can likely be attributed to stage-appropriate therapy due to more complete staging using FDG-PET/CT. This study stresses the importance of complete staging for suspected stage III NSCLC using FDG-PET/CT, and a need for continued advocacy for increased access to FDG-PET/CT scans.

Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors

Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.

Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

OBJECTIVE

To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma.

METHODS

A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients' clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test.

RESULTS

The proposed DL signature yielded an AUC of 0.948-0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature.

CONCLUSIONS

The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options.

KEY POINTS

• Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma. • The deep learning signature yielded an AUC of 0.948-0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model. • The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature.

Is Delayed Image of 18F-FDG PET/CT Necessary for Mediastinal Lymph Node Staging in Non-Small Cell Lung Cancer Patients?

PURPOSE

The purpose of this study was to evaluate the diagnostic accuracies of dual-time-point (DTP) 18F-FDG PET/CT for detection of mediastinal lymph node (LN) metastasis in non-small cell lung cancer (NSCLC) patients through a systematic review and meta-analysis.

PATIENTS AND METHODS

The PubMed, Cochrane database, and EMBASE database, from the earliest available date of indexing through October 31, 2021, were searched for studies evaluating diagnostic performance of DTP 18F-FDG PET/CT for detection of metastatic mediastinal LN in NSCLC patients. We determined the sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LR+ and LR-), and constructed summary receiver operating characteristic curves.

RESULTS

Ten studies (758 patients) were included in the current study. In patient-based analysis, early image showed a sensitivity of 0.76 and a specificity of 0.75. Delayed image revealed a sensitivity of 0.84 and a specificity of 0.71. In LN-based analysis, early image showed a sensitivity of 0.80 and a specificity of 0.83. Delayed image revealed a sensitivity of 0.84 and a specificity of 0.87. Retention index or %ΔSUVmax is superior to early or delayed images of DTP 18F-FDG PET/CT for detection of mediastinal LN metastasis.

CONCLUSIONS

Dual-time-point 18F-FDG PET/CT showed a good diagnostic performances for detection of metastatic mediastinal LNs in NSCLC patients. Early and delayed images of DTP 18F-FDG PET/CT revealed similar diagnostic accuracies for LN metastasis. However, retention index or %ΔSUVmax is superior to early or delayed images of DTP 18F-FDG PET/CT for detection of mediastinal LN metastasis in NSCLC patients. Further large multicenter studies would be necessary to substantiate the diagnostic accuracy of DTP 18F-FDG PET/CT for mediastinal LN staging in NSCLC patients.