NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022

Wood, Douglas E.; Kazerooni, Ella A.; Aberle, Denise R.; Berman, Abigail T.; Brown, Lisa M.; Eapen, Georgie A.; Ettinger, David S.; Ferguson, J. Scott; Hou, Lifang; Kadaria, Dipen; Klippenstein, Donald; Kumar, Rohit; Lackner, Rudy P.; Leard, L.E.; Lennes, Inga T.; Leung, Ann N.; Mazzone, Peter J.; Merritt, Robert E.; Midthun, David E.; Onaitis, Mark W.; Pipavath, Sudhakar; Pratt, Christie; Puri, Varun; Raz, Dan J.; Reddy, Chakravarthy; Reid, Mary E.; Sandler, Kim L.; Sands, Jacob; Schabath, Matthew B.; Studts, Jamie L.; Tanoue, Lynn K.; Tong, Betty C.; Travis, William D.; Wei, Benjamin; Westover, Kenneth D.; Yang, Stephen C.; McCullough, Beth; Hughes, Miranda

doi:10.6004/jnccn.2022.0036

Cited by 73 publications

(50 citation statements)

References 87 publications

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“…There are several models ( 22 – 24 ) which combined clinical and radiographic factors to estimate the malignancy probability and achieve fair performance ( 25 ). For those indeterminate nodules, follow-up would be recommended ( 26 ). The growth pattern of lung nodules ( 18 , 27 ) would increase the accuracy rate to diagnose malignant nodules and reduce the false positives, although some benign nodules would grow ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Liao

Yan

et al. 2022

Front. Oncol.

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BackgroundEstimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth and (2) seek to establish a deep learning-based model to predict the growth of SSNs.MethodsA total of 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules. A total of 2,358 patients with 3,120 SSNs from the NLST dataset were randomly divided into training and validation sets. Patients from the Yibicom Health Management Center and Guangdong Provincial People’s Hospital were collected as an external test set (165 patients with 213 SSN). Trained models based on LUNA16 and Lndb19 datasets were employed to automatically obtain the diameter, volume, and mass of SSNs. Then, the increase rate in measurements between cancer and non-cancer groups was studied to evaluate the most appropriate way to identify growth-associated lung cancer. Further, according to the selected measurement, all SSNs were classified into two groups: growth and non-growth. Based on the data, the deep learning-based model (SiamModel) and radiomics model were developed and verified.ResultsThe double time of diameter, volume, and mass were 711 vs. 963 days (P = 0.20), 552 vs. 621 days (P = 0.04) and 488 vs. 623 days (P< 0.001) in the cancer and non-cancer groups, respectively. Our proposed SiamModel performed better than the radiomics model in both the NLST validation set and external test set, with an AUC of 0.858 (95% CI 0.786–0.921) and 0.760 (95% CI 0.646–0.857) in the validation set and 0.862 (95% CI 0.789–0.927) and 0.681 (95% CI 0.506–0.841) in the external test set, respectively. Furthermore, our SiamModel could use the data from first-time CT to predict the growth of SSNs, with an AUC of 0.855 (95% CI 0.793–0.908) in the NLST validation set and 0.821 (95% CI 0.725–0.904) in the external test set.ConclusionMass increase rate can reflect more sensitively the growth of SSNs associated with lung cancer than diameter and volume increase rates. A deep learning-based model has a great potential to predict the growth of SSNs.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Liao

Yan

et al. 2022

Front. Oncol.

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“…However, the age of the screening population is not completely consistent. The latest NCCN guidelines suggest that 55-77 years old or older who are at greater risk of lung cancer should be screened ( 31 ). The International Early Lung Cancer Action Plan recommends LCS for people over 40 years old ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical features and surgical outcomes of young patients with lung adenocarcinoma manifesting as ground glass opacity

Cai

et al. 2022

Front. Oncol.

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BackgroundMore and more ground glass opacity associated lung adenocarcinoma (GGO-LUAD) have been diagnosed in young patients nowadays. Our study aims to investigate the clinical features and surgical outcomes of young patients with GGO-LUAD.MethodsPatients aged ≤ 40 years who were diagnosed as lung adenocarcinoma and who underwent video assisted thoracoscopic surgery (VATS) were retrospectively reviewed from January 2017 to December 2018. According to radiological appearance of the patient’s lesions, they were divided into a solid nodule (SN) group and GGO group. The pathological subtypes, surgical procedures and nodules size were analyzed, and the clinical features and prognosis were evaluated between these patients.ResultsA total of 165 patients were included, of which 133 were in the GGO group and 32 in the SN group. Both the GGO group and the SN group had a higher proportion of females and non-smokers. Compared with patients (15.63%) in the SN group, there are more patients (27.8%) under the age of 30 in the GGO group. Pathological findings showed 83.5% of lesions were pre-invasive lesions in the GGO group, although 16.5% of lesions were invasive adenocarcinoma, whereas in the SN group, 96.9% were invasive adenocarcinoma. The GGO group had significantly better histological characteristics and prognosis than the SN group. Perioperative complications occurred in only 6 patients, including pneumonia in one patient, pneumothorax in two patients, and prolonged air leak in three patients. No other serious complications or deaths occurred. After a median follow-up time of 41.2 ± 7.2 months (32-56), the 3-year recurrence free survival (RFS) (100%) and overall survival (OS) (100%) of the GGO group were significantly higher than those (93.42% and 96.88%) in the SN group.ConclusionsYoung patients with GGO-LUAD are mainly non-smokers and female. Most of these patients were early-stage with good prognosis after surgery.

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“…How to combine daily medical services with target disease screening to improve the early diagnosis rate of lung cancer in the general population is a problem that needs to be considered in the current lung cancer screening. At present, most lung cancer screening guidelines recommend 55 as the starting age for lung cancer screening ( 15 – 17 ). The onset age of lung cancer in China is about five years earlier than that in Europe and the United States ( 18 ).…”

Section: Discussionmentioning

confidence: 99%

Secondary preventing effect of lung cancer in non-high-risk population: A retrospective investigation of opportunistic screening with low-dose computed tomography in Wuhan

et al. 2022

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BackgroundGiven the mortality benefit of low-dose computed tomography (LDCT) screening on high-risk populations, the retrospective investigation intended to identify the benefits of LDCT on lung cancer screening among the general demographic cohorts.MethodsWe used an opportunistic screening with LDCT implemented during the pandemic in Wuhan to study the impact on subsequent thoracic surgeries, especially surgeries for lung cancer. Patients who received LDCT from October 1, 2019, to July 31, 2020, in three Triple-A accredited hospitals in Wuhan were included in the study. Relative week volumes of both surgeries before and after the chest LDCT screening were compared pairwise. The counts of surgeries for pulmonary nodules or masses, and corresponding pathological results among different gender and age groups were also compared.ResultThe relative weekly volumes of thoracic surgery were significantly greater than those of stomach surgery after the opportunistic screening with LDCT. They were 33% (95% CI, 0.20-0.46; p<0. 001) higher than those of stomach surgery. For every 1,000 chest LDCT scans conducted in a given week, on average, 3.52(95% CI,0.56-6.49, p =0.03) thoracic surgeries were performed in the following week. After the implementation of opportunistic screening with LDCT, there was a higher percentage of young females with pulmonary nodule or mass (64.4% vs. 45.8%, p = 0.032). The fraction of lung cancer surgery in the treatment period was significantly greater than that in the control period (74.09% vs. 68.79%, p=0.007). There was a higher percentage of stage I lung cancer surgery in young and mid-age females than in the senior age group (64% vs. 53%, p= 0.05).InterpretationOpportunistic screening with LDCT can advance the early diagnosis window of lung cancer in non-high-risk populations, especially young women who are easy to be ignored.

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NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022

Cited by 73 publications

References 87 publications

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Clinical features and surgical outcomes of young patients with lung adenocarcinoma manifesting as ground glass opacity

Secondary preventing effect of lung cancer in non-high-risk population: A retrospective investigation of opportunistic screening with low-dose computed tomography in Wuhan

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