BackgroundExtended or combined segmentectomies are usually adapted for intersegmental pulmonary nodules. This study explored precise combined subsegmentectomy (CSS) under the guidance of three‐dimensional computed tomography bronchography and angiography (3D‐CTBA).MethodsThe definition of a pulmonary intersegmental nodule was based on a minimum distance between the nodule and the involved intersegmental veins in the preoperative 3D‐CTBA being less than the size of the nodule. Centering on the involved intersegmental vein, two adjacent subsegments belonging to the different segments were combined as a resected unit.ResultsWe retrospectively reviewed the records of 47 patients (mean age 53.6 ± 12.3, range: 26–81 years) who underwent CSS. Thirty‐nine (83.0%) nodules were involved in most intersegmental locations of the upper lobes; the remainder in the lower lobes. The mean nodule size was 0.86 ± 0.32 cm; the mean margin width was 2.20 ± 0.38 cm. Pathological stages included: Tis (8 cases), T1mi (16), IA1 (T1aN0M0, 13), and IA2 (T1bN0M0, 5). Pathological diagnoses included: invasive adenocarcinoma (18 cases), minimally invasive adenocarcinoma (16), adenocarcinoma in situ (8), atypical adenomatous hyperplasia (3), and benign (2). The average operative duration was 190.8 ± 54.9 minutes; operative hemorrhage was 42.7 ± 23.0 mL; 5.8 ± 2.8 lymph nodes dissected had not metastasized; the duration of postoperative chest tube drainage was 3.0 ± 1.8 days; and the postoperative hospital stay was 5.3 ± 2.4 days.ConclusionsUnder 3D navigation, thoracoscopic CSS is a safe technique for intersegmental nodules, sparing more pulmonary parenchyma and ensuring safe margins to achieve anatomical resection.
Segmentectomy is a widely adopted surgical procedure, however, experiences of tailoring the intersegmental border have rarely been reported. This paper investigates the strategy and results of tailoring complex demarcation during lung segmentectomy surgery. Because intersegmental demarcation can be divided into plane or curved types according to the location and stereo shape of a segment, a one‐size‐fits‐all method for tailoring the intersegmental demarcation is obviously unreasonable. For tailoring a complex segmentectomy with two or more curved borders, tips including good exposure of the intersegmental demarcation, sharp‐blunt combined dissection skill, “work‐plane” extension, and “gate” opening techniques all contribute to an accurate segmentectomy. This technique, based on anatomical characteristics, can provide a cutting surface with a greater physiological shape and less curling of the edge, and should be recommended as a general standard method for tailoring complex demarcation.
Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.
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