Anatomical Pulmonary Sublobar Resection Based on Subsegment

Wu, Weibing; He, Zhicheng; Xu, Jianping; Wen, Wei; Wang, Jun; Zhu, Quan; Chen, Liang

doi:10.1016/j.athoracsur.2020.10.083

Cited by 17 publications

(16 citation statements)

References 5 publications

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“…A consensus on the definition of pulmonary intersegmental nodules has not been reached. In our centre, we have adopted the principle that if the virtual surgical margin at the 3D-CTBA, with the nodule as its centre, extends beyond the corresponding inter-SVs, the nodule is regarded as the intersegmental nodule [ 11 ]. Thus, being familiar with the relationship between the target nodule and the corresponding inter-SVs on 3D-CTBA images is helpful when designing a nodule-centred surgical resection range.…”

Section: Discussionmentioning

confidence: 99%

“…The method of determining the surgical margin was described previously [ 11 ]. The surgical procedure was simulated based on nodule location and margin distance in the 3D-CTBA.…”

Section: Methodsmentioning

confidence: 99%

“…In this situation, S 10 plus an adjacent segment or subsegment (individualized S 10 ) may be a wiser option. We previously reported how we categorized pulmonary nodules into different types based on the relationship of the nodules and vessels in three-dimensional (3D) CT bronchography and angiography (3D-CTBA) [ 11 ]. Nevertheless, how to technically remove such nodules has not been described in detail.…”

Section: Introductionmentioning

confidence: 99%

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Individualized dorsal basal segment (S10) resection using intersegmental veins as the landmark

Pan

et al. 2021

Interactive CardioVascular and Thoracic Surgery

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OBJECTIVES The individualized thoracoscopic dorsal basal (S10) resection remains one of the most challenging procedures. Our goal was to detail the role of intersegmental veins (inter-SVs) in facilitating such a complex operation and evaluate its safety and efficacy. METHODS We retrospectively reviewed patients who underwent S10 or S10 plus an adjacent segment or subsegment resection (individualized S10) from January 2015 through September 2020. Individualized S10 resections were conducted for nodules of 2 cm or less with a ground-glass opacity evident in thin-slice computed tomography. A simplified method of using inter-SVs as the landmark in surgical planning and segmentectomy was described. The efficacy and safety of this technique were also evaluated in comparison with those aspects of the lower lobectomy. RESULTS In total, 46 patients who underwent individualized S10 through an inferior pulmonary ligament approach were included. All patients received R0 resection with a surgical margin of 22.45 mm. No patient was converted to an extended resection such as an entire basal or lower lobar resection. Three patients whose situation was complicated with an air leak had non-urgent interventions. Comparable results were obtained between the segmental and lobar arms in terms of blood loss (49.13 vs 45.98 ml), postoperative hospital stay (4.96 vs 5.18 days) and persistent air leak (6.52% vs 4.01%). CONCLUSIONS A strategy guided by the inter-SVs permits one to tailor the surgical planning for S10 nodules without compromising the surgical margin. It could also facilitate target bronchial recognition and intersegmental plane division. However, long-term follow-up and large clinical studies are needed to further justify its clinical benefits.

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Section: Discussionmentioning

confidence: 99%

“…The method of determining the surgical margin was described previously [ 11 ]. The surgical procedure was simulated based on nodule location and margin distance in the 3D-CTBA.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Individualized dorsal basal segment (S10) resection using intersegmental veins as the landmark

Pan

et al. 2021

Interactive CardioVascular and Thoracic Surgery

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“…Such studies still have the following limitations: (I) the use of lung cancer screening test data showed regional bias; (II) fail to conduct adequate clinical comparison and practical application value assessment; (III) less assessment of the survival and prognosis of patients was carried out. The above problems have caused great resistance to CT radiomics analyses in the early stage of lung cancer (58). After all, there are much more challenges than the ones mentioned.…”

Section: Challenges Of Ct Radiomics Analyses In Early Stage Lung Cancermentioning

confidence: 99%

Early-stage lung cancer detection from radiomics to deep learning in thoracic CT images: a narrative review with contemporary clinical recommendations

Yang¹,

She²,

Ren³

et al. 2021

Shanghai Chest

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The accurate identification and characterization of pulmonary nodules at low-dose chest computed tomography (CT) images is an essential requirement for the implementation of effective lung cancer screening. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. Different nodule detection approaches are described elaborately in this work. Computer-aided diagnosis system acts as an assistance for the radiologists, by making final decision quickly with higher accuracy and greater confidence. Scholars have proposed that a large number of high-dimensional quantitative features can be mined and combined with statistical models to comprehensively classify medical tumor images, namely, radiomics. Many researches have already demonstrated that radiomics approaches are potentially useful for lung nodule classification, risk stratification, individualized management and prediction of outcome. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks. Hence, the development of non-invasive strategies to accurately classify and risk stratify screen-detected pulmonary nodules in order to individualize clinical management remains a high priority area of research. The goal of this review is to summarize the current literature regarding functional imaging for screening detected lung nodule management with CT and discuss its clinical application along with future goals and challenges.

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“…In such cases, combined subsegmentectomy becomes the appropriate therapy of choice. 2 As thoracoscopic anatomical basal segmentectomies, those involving the lateral (S9) and/or posterior (S10) basal segments are technically challenging, 3 and combined subsegmentectomy involving S9b and S10b would be one of the most technically demanding ones. To the best of our knowledge, there has been no report on it.…”

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confidence: 99%

Thoracoscopic complex pulmonary basal subsegmentectomy: A combined subsegmentectomy of left s9b+10b

2022

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Anatomical Pulmonary Sublobar Resection Based on Subsegment

Cited by 17 publications

References 5 publications

Individualized dorsal basal segment (S10) resection using intersegmental veins as the landmark

Individualized dorsal basal segment (S10) resection using intersegmental veins as the landmark

Early-stage lung cancer detection from radiomics to deep learning in thoracic CT images: a narrative review with contemporary clinical recommendations

Thoracoscopic complex pulmonary basal subsegmentectomy: A combined subsegmentectomy of left s9b+10b

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