Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy
Abstract Objectives This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment. Materials and methods In a training cohort o...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-10-01
|
| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-023-00620-4 |
| _version_ | 1797451675022131200 |
|---|---|
| author | Runsheng Chang Shouliang Qi Yanan Wu Yong Yue Xiaoye Zhang Wei Qian |
| author_facet | Runsheng Chang Shouliang Qi Yanan Wu Yong Yue Xiaoye Zhang Wei Qian |
| author_sort | Runsheng Chang |
| collection | DOAJ |
| description | Abstract Objectives This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment. Materials and methods In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients). Results TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40–3.67); PFS: (HR (95%), 2.23 (1.36–3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70–0.79) and 0.72 (0.67–0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68–0.81) and 0.72 (0.66–0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS. Conclusion By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients. |
| first_indexed | 2024-03-09T14:56:55Z |
| format | Article |
| id | doaj.art-35e0b18a9cc44d55bf5137889ce92f83 |
| institution | Directory Open Access Journal |
| issn | 1470-7330 |
| language | English |
| last_indexed | 2024-03-09T14:56:55Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj.art-35e0b18a9cc44d55bf5137889ce92f832023-11-26T14:06:23ZengBMCCancer Imaging1470-73302023-10-0123111210.1186/s40644-023-00620-4Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapyRunsheng Chang0Shouliang Qi1Yanan Wu2Yong Yue3Xiaoye Zhang4Wei Qian5College of Medicine and Biological Information Engineering, Northeastern UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityDepartment of Radiology, Shengjing Hospital of China Medical UniversityDepartment of Oncology, Shengjing Hospital of China Medical UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityAbstract Objectives This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment. Materials and methods In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients). Results TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40–3.67); PFS: (HR (95%), 2.23 (1.36–3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70–0.79) and 0.72 (0.67–0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68–0.81) and 0.72 (0.66–0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS. Conclusion By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.https://doi.org/10.1186/s40644-023-00620-4NomogramOverall survivalProgression-free survivalLung cancerChemotherapy treatmentRadiomics |
| spellingShingle | Runsheng Chang Shouliang Qi Yanan Wu Yong Yue Xiaoye Zhang Wei Qian Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy Cancer Imaging Nomogram Overall survival Progression-free survival Lung cancer Chemotherapy treatment Radiomics |
| title | Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy |
| title_full | Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy |
| title_fullStr | Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy |
| title_full_unstemmed | Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy |
| title_short | Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy |
| title_sort | nomograms integrating ct radiomic and deep learning signatures to predict overall survival and progression free survival in nsclc patients treated with chemotherapy |
| topic | Nomogram Overall survival Progression-free survival Lung cancer Chemotherapy treatment Radiomics |
| url | https://doi.org/10.1186/s40644-023-00620-4 |
| work_keys_str_mv | AT runshengchang nomogramsintegratingctradiomicanddeeplearningsignaturestopredictoverallsurvivalandprogressionfreesurvivalinnsclcpatientstreatedwithchemotherapy AT shouliangqi nomogramsintegratingctradiomicanddeeplearningsignaturestopredictoverallsurvivalandprogressionfreesurvivalinnsclcpatientstreatedwithchemotherapy AT yananwu nomogramsintegratingctradiomicanddeeplearningsignaturestopredictoverallsurvivalandprogressionfreesurvivalinnsclcpatientstreatedwithchemotherapy AT yongyue nomogramsintegratingctradiomicanddeeplearningsignaturestopredictoverallsurvivalandprogressionfreesurvivalinnsclcpatientstreatedwithchemotherapy AT xiaoyezhang nomogramsintegratingctradiomicanddeeplearningsignaturestopredictoverallsurvivalandprogressionfreesurvivalinnsclcpatientstreatedwithchemotherapy AT weiqian nomogramsintegratingctradiomicanddeeplearningsignaturestopredictoverallsurvivalandprogressionfreesurvivalinnsclcpatientstreatedwithchemotherapy |