scholarly journals Inferior Pulmonary Ligament Division May Be Unnecessary during Left Upper Lobectomy: Effects on Lung Volume, Bronchial Angle and Bronchial Tortuosity

2021 ◽  
Vol 10 (18) ◽  
pp. 4033
Author(s):  
Duk Hwan Moon ◽  
Chul Hwan Park ◽  
Joon Ho Jung ◽  
Tae Hoon Kim ◽  
Seok Jin Haam ◽  
...  

The benefits of dissecting inferior pulmonary ligament (IPL) during upper lobectomy using video-assisted thoracoscopic surgery (VATS) for early-stage lung cancer remains controversial. This study evaluates the effect of IPL dissection by comparing the lung volume, bronchial angle, and bronchial tortuosity of the left lower lobe (LLL) during VATS upper lobectomy. Medical records of all patients who underwent VATS left upper lobectomy for early-stage lung cancer were evaluated. Patients were divided into group P (preservation) and group D (dissection). Pre- and post-surgery lung volumes, bronchial angles (angle 1: axial angulation; angle 2: vertical angulation), and bronchial tortuosity (curvature index of the left main bronchus) were measured using computed tomography images for comparison. Forty patients were included in each group. Patient characteristics such as age, gender, body mass index, and smoking status, and preoperative lung volume, bronchial angles, and tortuosity were not significantly different between the two groups, and there was no statistically significant difference in the axial and vertical angulations; however, the change in pre- and postoperative bronchial tortuosity (0.03 ± 0.03 vs. 0.06 ± 0.03) and lung volume (−558.1 ± 410.0 mL vs. −736.3 ± 382.7 mL) showed a significant difference (p < 0.001 and p = 0.04, respectively). Preservation of IPLs during left upper lobectomy may be beneficial for LLL expansion and induces less movement and positional change in the left main bronchus.

2017 ◽  
Vol 117 (4) ◽  
pp. 618-624 ◽  
Author(s):  
Vittorio Aprile ◽  
Pietro Bertoglio ◽  
Paolo Dini ◽  
Gerardo Palmiero ◽  
Alfredo Mussi ◽  
...  

2021 ◽  
Vol 16 (3) ◽  
pp. S264-S265
Author(s):  
F. Xu ◽  
L. Yang ◽  
C. Liu ◽  
J. Ying ◽  
Y. Wang

Author(s):  
Guangyao Wu ◽  
Arthur Jochems ◽  
Turkey Refaee ◽  
Abdalla Ibrahim ◽  
Chenggong Yan ◽  
...  

Abstract Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”


2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Seijiro Sato ◽  
Masaya Nakamura ◽  
Yuki Shimizu ◽  
Tatsuya Goto ◽  
Terumoto Koike ◽  
...  

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