AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels

Artificial Intelligence in Surgery: A Systematic Review of Use and Validation

Background: Artificial Intelligence (AI) holds promise for transforming healthcare, with AI models gaining increasing clinical use in surgery. However, new AI models are developed without established standards for their validation and use. Before AI can be widely adopted, it is crucial to ensure these models are both accurate and safe for patients. Without proper validation, there is a risk of integrating AI models into practice without sufficient evidence of their safety and accuracy, potentially leading to suboptimal patient outcomes. In this work, we review the current use and validation methods of AI models in clinical surgical settings and propose a novel classification system. Methods: A systematic review was conducted in PubMed and Cochrane using the keywords "validation", "artificial intelligence", and "surgery", following PRISMA guidelines. Results: The search yielded a total of 7627 articles, of which 102 were included for data extraction, encompassing 2,837,211 patients. A validation classification system named Surgical Validation Score (SURVAS) was developed. The primary applications of models were risk assessment and decision-making in the preoperative setting. Validation methods were ranked as high evidence in only 45% of studies, and only 14% of the studies provided publicly available datasets. Conclusions: AI has significant applications in surgery, but validation quality remains suboptimal, and public data availability is limited. Current AI applications are mainly focused on preoperative risk assessment and are suggested to improve decision-making. Classification systems such as SURVAS can help clinicians confirm the degree of validity of AI models before their application in practice.

Advances in artificial intelligence applications in the field of lung cancer

Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment

The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.

A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma

OBJECTIVES

In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT).

MATERIALS AND METHODS

CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE).

RESULTS

A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908-0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%).

CONCLUSION

A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC.

CLINICAL RELEVANCE

The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.

Harnessing 3D-CT Simulation and Planning for Enhanced Precision Surgery: A Review of Applications and Advancements in Lung Cancer Treatment

The clinical application of three-dimensional computed tomography (3D-CT) technology has rapidly expanded in the last decade and has been applied to lung cancer surgery. Two consecutive reports of large-scale prospective clinical trials from Japan and the United States have brought a paradigm shift in lung cancer surgery and may have led to a rapid increase in sublobar lung resections. Sublobar resection, especially segmentectomy, requires a more precise understanding of the anatomy than lobectomy, and preoperative 3D simulation and intraoperative navigation support it. The latest 3D simulation software packages are user-friendly. Therefore, in this narrative review, we focus on recent attempts to apply 3D imaging technologies, particularly in the sublobar resection of the lung, and review respective research and outcomes. Improvements in CT accuracy and the use of 3D technology have advanced lung segmental anatomy. Clinical applications have enabled the safe execution of complex sublobar resection through a minimally invasive approach, such as video-assisted thoracoscopic surgery and robotic surgery. However, currently, many facilities still render 3D images on two-dimensional monitors for usage. In the future, it will be challenging to further spread and advance intraoperative navigation through the application of 3D output technologies such as extended reality.

An atlas of anatomical variants of subsegmental pulmonary arteries and recognition error analysis

Background

Surgery, including lobectomy and segmentectomy, is the major curative intervention for lung cancer. Surgical planning for pulmonary surgery is difficult due to the high variation rate of pulmonary arteries and needs a fine-grained atlas as a reference. We conducted a study to create a surgically oriented atlas and analyzed the error encountered during the production.

Method

A total of 100 Chest CTs performed at Peking University People’s Hospital from 2013.09 to 2020.10 were randomly selected for segmental artery labeling. Dicom files were collected for 3D reconstruction. Manual segmentation of each segmental artery was performed by 4 thoracic surgeons. Cross-validation by surgeons was performed to establish the golden standard based on their consensus. Initial recognition errors were recorded accordingly.

Result

The most frequently seen variants for the right upper lobe is 2-branch RA1+2rec+3 and RA2asc; right middle lobe 2-branch RA4a and RA4b+5; right lower lobe 3-branch RA7, RA8 and RA9+10; left upper lobe 3-branch LA1+2a+3, LA1+2b, LA1+2c and 1-branch LA4+5; left lower lobe 2-branch LA8 and LA9+10. Top 5 segmental error occurs in RA4 (23%), LA8 (17%), RA9 (17%), RA8 (14%) and LA9 (11%). A rapid surgical planning tool form was created based on high frequency anatomic variants.

Conclusion

Our research provided an atlas for lobectomy and segmentectomy at the subsegmental or more distal level. We demonstrated that the recognition accuracy of pulmonary arteries in a non-time-sensitive experimental scenario was still unfavorable. We also suggest that extra attention should be paid to certain surgeries during the surgical planning process.