Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis
Abstract In the 8th edition of the American Joint Committee on Cancer (AJCC) staging system for Non-Small Cell Lung Cancer (NSCLC), tumors exhibiting main bronchial infiltration (MBI) near the carina and those presenting with complete lung obstructive pneumonia/atelectasis (P/ATL) have been reclassi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-55507-6 |
| _version_ | 1797274591584845824 |
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| author | Xuanhong Jin Yang Pan Chongya Zhai Hangchen shen Liangkun You Hongming Pan |
| author_facet | Xuanhong Jin Yang Pan Chongya Zhai Hangchen shen Liangkun You Hongming Pan |
| author_sort | Xuanhong Jin |
| collection | DOAJ |
| description | Abstract In the 8th edition of the American Joint Committee on Cancer (AJCC) staging system for Non-Small Cell Lung Cancer (NSCLC), tumors exhibiting main bronchial infiltration (MBI) near the carina and those presenting with complete lung obstructive pneumonia/atelectasis (P/ATL) have been reclassified from T3 to T2. Our investigation into the Surveillance, Epidemiology, and End Results (SEER) database, spanning from 2007 to 2015 and adjusted via Propensity Score Matching (PSM) for additional variables, disclosed a notably inferior overall survival (OS) for patients afflicted with these conditions. Specifically, individuals with P/ATL experienced a median OS of 12 months compared to 15 months (p < 0.001). In contrast, MBI patients demonstrated a slightly worse prognosis with a median OS of 22 months versus 23 months (p = 0.037), with both conditions significantly correlated with lymph node metastasis (All p < 0.001). Upon evaluating different treatment approaches for these particular T2 NSCLC variants, while adjusting for other factors, surgery emerged as the optimal therapeutic strategy. We counted those who underwent surgery and found that compared to surgery alone, the MBI/(P/ATL) group experienced a much higher proportion of preoperative induction therapy or postoperative adjuvant therapy than the non-MBI/(P/ATL) group (41.3%/54.7% vs. 36.6%). However, for MBI patients, initial surgery followed by adjuvant treatment or induction therapy succeeded in significantly enhancing prognosis, a benefit that was not replicated for P/ATL patients. Leveraging the XGBoost model for a 5-year survival forecast and treatment determination for P/ATL and MBI patients yielded Area Under the Curve (AUC) scores of 0.853 for P/ATL and 0.814 for MBI, affirming the model's efficacy in prognostication and treatment allocation for these distinct T2 NSCLC categories. |
| first_indexed | 2024-03-07T15:01:30Z |
| format | Article |
| id | doaj.art-8dfe5d72a35e40e0b6e311f7809fe478 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:01:30Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8dfe5d72a35e40e0b6e311f7809fe4782024-03-05T19:08:45ZengNature PortfolioScientific Reports2045-23222024-02-0114111210.1038/s41598-024-55507-6Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasisXuanhong Jin0Yang Pan1Chongya Zhai2Hangchen shen3Liangkun You4Hongming Pan5Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang UniversityPostgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital)Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang UniversityAbstract In the 8th edition of the American Joint Committee on Cancer (AJCC) staging system for Non-Small Cell Lung Cancer (NSCLC), tumors exhibiting main bronchial infiltration (MBI) near the carina and those presenting with complete lung obstructive pneumonia/atelectasis (P/ATL) have been reclassified from T3 to T2. Our investigation into the Surveillance, Epidemiology, and End Results (SEER) database, spanning from 2007 to 2015 and adjusted via Propensity Score Matching (PSM) for additional variables, disclosed a notably inferior overall survival (OS) for patients afflicted with these conditions. Specifically, individuals with P/ATL experienced a median OS of 12 months compared to 15 months (p < 0.001). In contrast, MBI patients demonstrated a slightly worse prognosis with a median OS of 22 months versus 23 months (p = 0.037), with both conditions significantly correlated with lymph node metastasis (All p < 0.001). Upon evaluating different treatment approaches for these particular T2 NSCLC variants, while adjusting for other factors, surgery emerged as the optimal therapeutic strategy. We counted those who underwent surgery and found that compared to surgery alone, the MBI/(P/ATL) group experienced a much higher proportion of preoperative induction therapy or postoperative adjuvant therapy than the non-MBI/(P/ATL) group (41.3%/54.7% vs. 36.6%). However, for MBI patients, initial surgery followed by adjuvant treatment or induction therapy succeeded in significantly enhancing prognosis, a benefit that was not replicated for P/ATL patients. Leveraging the XGBoost model for a 5-year survival forecast and treatment determination for P/ATL and MBI patients yielded Area Under the Curve (AUC) scores of 0.853 for P/ATL and 0.814 for MBI, affirming the model's efficacy in prognostication and treatment allocation for these distinct T2 NSCLC categories.https://doi.org/10.1038/s41598-024-55507-6 |
| spellingShingle | Xuanhong Jin Yang Pan Chongya Zhai Hangchen shen Liangkun You Hongming Pan Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis Scientific Reports |
| title | Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis |
| title_full | Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis |
| title_fullStr | Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis |
| title_full_unstemmed | Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis |
| title_short | Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis |
| title_sort | exploration and machine learning model development for t2 nsclc with bronchus infiltration and obstructive pneumonia atelectasis |
| url | https://doi.org/10.1038/s41598-024-55507-6 |
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