The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model
Abstract Background The prediction of postoperative respiratory function is necessary in identifying patients that are at greater risk of complications. There are not enough studies on the effect of the diaphragm on postoperative respiratory function prediction in lung cancer surgical patients. The...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-12-01
|
| Series: | World Journal of Surgical Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12957-023-03278-1 |
| _version_ | 1797376999744864256 |
|---|---|
| author | Radomir Vesovic Milan Milosavljevic Marija Punt Jelica Radomirovic Slavisa Bascarevic Milan Savic Vladimir Milenkovic Marko Popovic Maja Ercegovac |
| author_facet | Radomir Vesovic Milan Milosavljevic Marija Punt Jelica Radomirovic Slavisa Bascarevic Milan Savic Vladimir Milenkovic Marko Popovic Maja Ercegovac |
| author_sort | Radomir Vesovic |
| collection | DOAJ |
| description | Abstract Background The prediction of postoperative respiratory function is necessary in identifying patients that are at greater risk of complications. There are not enough studies on the effect of the diaphragm on postoperative respiratory function prediction in lung cancer surgical patients. The objective of this study is to estimate the precision of machine learning methods in the prediction of respiratory function in the immediate postoperative period and how diaphragm function contributes to that prediction. Materials and methods Our prospective study included 79 patients who underwent lung cancer surgery. Diaphragm function was estimated by its mobility measured both ultrasonographically and radiographically and by noninvasive muscle strength tests. We present a new machine learning multilayer regression metamodel, which predicts FEV1 for each patient based on preoperative measurements. Results The proposed regression models are specifically trained to predict FEV1 in the immediate postoperative period and were proved to be highly accurate (mean absolute error in the range from 8 to 11%). Predictive models based on resected segments give two to three times less precise results. Measured FEV1 was 44.68% ± 14.07%, 50.95% ± 15.80%, and 58.0%1 ± 14.78%, and predicted postoperative (ppo) FEV1 was 43.85% ± 8.80%, 50.62% ± 9.28%, and 57.85% ± 10.58% on the first, fourth, and seventh day, respectively. By interpreting the obtained model, the diaphragm contributes to ppoFEV1 13.62% on the first day, 10.52% on the fourth, and 9.06% on the seventh day. Conclusion The machine learning metamodel gives more accurate predictions of postoperative lung function than traditional calculations. The diaphragm plays a notable role in the postoperative FEV1 prediction. |
| first_indexed | 2024-03-08T19:46:39Z |
| format | Article |
| id | doaj.art-be51a14bb0c94c92808f3460484f449d |
| institution | Directory Open Access Journal |
| issn | 1477-7819 |
| language | English |
| last_indexed | 2024-03-08T19:46:39Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Surgical Oncology |
| spelling | doaj.art-be51a14bb0c94c92808f3460484f449d2023-12-24T12:20:36ZengBMCWorld Journal of Surgical Oncology1477-78192023-12-0121111110.1186/s12957-023-03278-1The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning modelRadomir Vesovic0Milan Milosavljevic1Marija Punt2Jelica Radomirovic3Slavisa Bascarevic4Milan Savic5Vladimir Milenkovic6Marko Popovic7Maja Ercegovac8Faculty of Medicine, University of BelgradeVlatacom Institute of High TechnologySchool of Electrical Engineering, University of BelgradeVlatacom Institute of High TechnologyFaculty of Medicine, University of BelgradeFaculty of Medicine, University of BelgradeFaculty of Medicine, University of BelgradeClinic for Thoracic Surgery, University Clinical Center of SerbiaFaculty of Medicine, University of BelgradeAbstract Background The prediction of postoperative respiratory function is necessary in identifying patients that are at greater risk of complications. There are not enough studies on the effect of the diaphragm on postoperative respiratory function prediction in lung cancer surgical patients. The objective of this study is to estimate the precision of machine learning methods in the prediction of respiratory function in the immediate postoperative period and how diaphragm function contributes to that prediction. Materials and methods Our prospective study included 79 patients who underwent lung cancer surgery. Diaphragm function was estimated by its mobility measured both ultrasonographically and radiographically and by noninvasive muscle strength tests. We present a new machine learning multilayer regression metamodel, which predicts FEV1 for each patient based on preoperative measurements. Results The proposed regression models are specifically trained to predict FEV1 in the immediate postoperative period and were proved to be highly accurate (mean absolute error in the range from 8 to 11%). Predictive models based on resected segments give two to three times less precise results. Measured FEV1 was 44.68% ± 14.07%, 50.95% ± 15.80%, and 58.0%1 ± 14.78%, and predicted postoperative (ppo) FEV1 was 43.85% ± 8.80%, 50.62% ± 9.28%, and 57.85% ± 10.58% on the first, fourth, and seventh day, respectively. By interpreting the obtained model, the diaphragm contributes to ppoFEV1 13.62% on the first day, 10.52% on the fourth, and 9.06% on the seventh day. Conclusion The machine learning metamodel gives more accurate predictions of postoperative lung function than traditional calculations. The diaphragm plays a notable role in the postoperative FEV1 prediction.https://doi.org/10.1186/s12957-023-03278-1DiaphragmRespiratory functionMachine learningLung cancerPrediction |
| spellingShingle | Radomir Vesovic Milan Milosavljevic Marija Punt Jelica Radomirovic Slavisa Bascarevic Milan Savic Vladimir Milenkovic Marko Popovic Maja Ercegovac The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model World Journal of Surgical Oncology Diaphragm Respiratory function Machine learning Lung cancer Prediction |
| title | The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model |
| title_full | The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model |
| title_fullStr | The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model |
| title_full_unstemmed | The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model |
| title_short | The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model |
| title_sort | role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model |
| topic | Diaphragm Respiratory function Machine learning Lung cancer Prediction |
| url | https://doi.org/10.1186/s12957-023-03278-1 |
| work_keys_str_mv | AT radomirvesovic theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT milanmilosavljevic theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT marijapunt theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT jelicaradomirovic theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT slavisabascarevic theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT milansavic theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT vladimirmilenkovic theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT markopopovic theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT majaercegovac theroleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT radomirvesovic roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT milanmilosavljevic roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT marijapunt roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT jelicaradomirovic roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT slavisabascarevic roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT milansavic roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT vladimirmilenkovic roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT markopopovic roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel AT majaercegovac roleofthediaphragminpredictionofrespiratoryfunctionintheimmediatepostoperativeperiodinlungcancerpatientsusingamachinelearningmodel |