Lung Resection Without Tissue Diagnosis: A Pragmatic Perspective on the Indeterminate Pulmonary Nodule

Lung Cancer resection in the absence of pre-operative histology: The Accuracy of Multidisciplinary Team Consensus

BACKGROUND

Lung resection remains the gold-standard of treatment for non-small cell lung cancer (NSCLC). British Thoracic Society (BTS) guidelines recommends the pursuit of pre-operative histological diagnosis and staging where possible. In the absence of pre-operative histology, surgical treatment can be offered in conjunction with multidisciplinary team (MDT) and patient consensus. We undertook a single-centre analysis of the accuracy of the lung cancer MDT in recommending surgical resection for those with suspected lung malignancy in the absence of pre-operative histological diagnosis over a six-year period.

METHODS

A retrospective review was performed of patients undergoing any pulmonary oncological resection with therapeutic intent without pre-operative histological diagnosis at the recommendation of the lung cancer MDT in our unit between May 2016 and July 2022.

RESULTS

270 consecutive patients underwent lung resection for a lung nodule of indeterminate significance. This accounted for 45% of the oncological resections performed over this period. The mean age of the cohort was 67.9 years, and 47.4% were male. Overall, 10% of resected specimens (n = 27) were benign on final histopathology. 93% of those undergoing a lobectomy received a malignant diagnosis. Across the study cohort, surgical resection was well tolerated with a low complication rate.

CONCLUSIONS

Lung cancer resection in the absence of pre-operative histological diagnosis is feasible in a select patient cohort. This approach requires an experienced multi-disciplinary team and careful patient counselling. Our study demonstrates this adapted approach to be a pragmatic solution to the management of indeterminate pulmonary nodules in centres where biopsy is not routinely available due to existing constraints on the health system.

The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the "Real World"?

(1) Background: Once lung lesions are identified on CT scans, they must be characterized by assessing the risk of malignancy. Despite the promising performance of computer-aided systems, some limitations related to the study design and technical issues undermine these tools' efficiency; an "intelligent agent" to detect and non-invasively characterize lung lesions on CT scans is proposed. (2) Methods: Two main modules tackled the detection of lung nodules on CT scans and the diagnosis of each nodule into benign and malignant categories. Computer-aided detection (CADe) and computer aided-diagnosis (CADx) modules relied on deep learning techniques such as Retina U-Net and the convolutional neural network; (3) Results: Tests were conducted on one publicly available dataset and two local datasets featuring CT scans acquired with different devices to reveal deep learning performances in "real-world" clinical scenarios. The CADe module reached an accuracy rate of 78%, while the CADx's accuracy, specificity, and sensitivity stand at 80%, 73%, and 85.7%, respectively; (4) Conclusions: Two different deep learning techniques have been adapted for CADe and CADx purposes in both publicly available and private CT scan datasets. Experiments have shown adequate performance in both detection and diagnosis tasks. Nevertheless, some drawbacks still characterize the supervised learning paradigm employed in networks such as CNN and Retina U-Net in real-world clinical scenarios, with CT scans from different devices with different sensors' fingerprints and spatial resolution. Continuous reassessment of CADe and CADx's performance is needed during their implementation in clinical practice.