Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors
<b>Background/Objectives</b>: This study aimed to investigate whether small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) can be distinguished based on radiomics data derived from T2-FLAIR digital subtraction images of the peritumoral edema region in patients with brain...
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MDPI AG
2025-05-01
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| Series: | Diagnostics |
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| author | Okan Dilek Emin Demırel Zeynel Abidin Tas Emre Bılgın |
| author_facet | Okan Dilek Emin Demırel Zeynel Abidin Tas Emre Bılgın |
| author_sort | Okan Dilek |
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| description | <b>Background/Objectives</b>: This study aimed to investigate whether small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) can be distinguished based on radiomics data derived from T2-FLAIR digital subtraction images of the peritumoral edema region in patients with brain metastases. <b>Methods:</b> A total of 136 patients who underwent surgery for brain tumors, including 100 patients in the Pretreat-Metstobrain-MASKS dataset and 36 patients from our institution, were included in our study. Radiomic features were extracted from digitally subtracted T2-FLAIR images in the peritumoral edema area. Patients were divided into NSCLC and SCLC groups. The maximum relevance–minimum redundancy (mRMR) method was then used for dimensionality reduction. The Naive Bayes algorithm was used for model development, and the interpretability of the model was explored using SHapley Additive exPlanations (SHAP). The performance metrics included the area under the curve (AUC), sensitivity (SENS), and specificity (SPEC). <b>Results:</b> The mean age of NSCLC patients was 64.6 ± 10.3 years, and that of SCLC patients was 63.4 ± 11.7 years. In the external validation cohort, the model achieved an AUC of 0.82 (0.68–0.97), a SENS of 0.87 (0.74–0.91), and a SPEC of 0.72 (0.72–0.89). In the train cohort, the model achieved an AUC of 1.000, a SENS of 1.000, and a SPEC of 1.000. The feature providing the best effect was wavelet-HHHglcmJointEnergy, with a SHAP value of approximately 2.5. <b>Conclusions:</b> An artificial intelligence model developed using radiomics data from T2-FLAIR digital subtraction images of the peritumoral edema area can identify the histologic type of lung cancer in patients with associated brain metastases. |
| format | Article |
| id | doaj-art-cf8a7a37e85e4fc4bc8ee5300c9dfa3e |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-cf8a7a37e85e4fc4bc8ee5300c9dfa3e2025-08-20T01:56:31ZengMDPI AGDiagnostics2075-44182025-05-011510128310.3390/diagnostics15101283Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain TumorsOkan Dilek0Emin Demırel1Zeynel Abidin Tas2Emre Bılgın3Department of Radiology, Adana City Training and Research Hospital, University of Health Sciences, 01370 Adana, TurkeyDepartment of Radiology, Faculty of Medicine, Afyonkarahisar University of Health Sciences, 03030 Afyonkarahisar, TurkeyDepartment Pathology, Adana Teaching and Research Hospital, University of Health Sciences, 01230 Adana, TurkeyDepartment Neurosurgery, Adana Teaching and Research Hospital, University of Health Sciences, 01230 Adana, Turkey<b>Background/Objectives</b>: This study aimed to investigate whether small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) can be distinguished based on radiomics data derived from T2-FLAIR digital subtraction images of the peritumoral edema region in patients with brain metastases. <b>Methods:</b> A total of 136 patients who underwent surgery for brain tumors, including 100 patients in the Pretreat-Metstobrain-MASKS dataset and 36 patients from our institution, were included in our study. Radiomic features were extracted from digitally subtracted T2-FLAIR images in the peritumoral edema area. Patients were divided into NSCLC and SCLC groups. The maximum relevance–minimum redundancy (mRMR) method was then used for dimensionality reduction. The Naive Bayes algorithm was used for model development, and the interpretability of the model was explored using SHapley Additive exPlanations (SHAP). The performance metrics included the area under the curve (AUC), sensitivity (SENS), and specificity (SPEC). <b>Results:</b> The mean age of NSCLC patients was 64.6 ± 10.3 years, and that of SCLC patients was 63.4 ± 11.7 years. In the external validation cohort, the model achieved an AUC of 0.82 (0.68–0.97), a SENS of 0.87 (0.74–0.91), and a SPEC of 0.72 (0.72–0.89). In the train cohort, the model achieved an AUC of 1.000, a SENS of 1.000, and a SPEC of 1.000. The feature providing the best effect was wavelet-HHHglcmJointEnergy, with a SHAP value of approximately 2.5. <b>Conclusions:</b> An artificial intelligence model developed using radiomics data from T2-FLAIR digital subtraction images of the peritumoral edema area can identify the histologic type of lung cancer in patients with associated brain metastases.https://www.mdpi.com/2075-4418/15/10/1283digital subtraction imagingsmall-cell lung cancernon-small-cell lung cancerradiomicsAI |
| spellingShingle | Okan Dilek Emin Demırel Zeynel Abidin Tas Emre Bılgın Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors Diagnostics digital subtraction imaging small-cell lung cancer non-small-cell lung cancer radiomics AI |
| title | Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors |
| title_full | Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors |
| title_fullStr | Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors |
| title_full_unstemmed | Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors |
| title_short | Exploring the Role of Peritumoral Edema in Predicting Lung Cancer Subtypes Through T2-FLAIR Digital Subtraction Imaging of Metastatic Brain Tumors |
| title_sort | exploring the role of peritumoral edema in predicting lung cancer subtypes through t2 flair digital subtraction imaging of metastatic brain tumors |
| topic | digital subtraction imaging small-cell lung cancer non-small-cell lung cancer radiomics AI |
| url | https://www.mdpi.com/2075-4418/15/10/1283 |
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