Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes
Objective. To develop software to assess the potential aggressiveness of an incidentally detected renal mass using images. Methods. Thirty randomly selected patients who underwent nephrectomy for renal cell carcinoma (RCC) had their images independently reviewed by engineers. Tumor “Roughness” was b...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Urology |
| Online Access: | http://dx.doi.org/10.1155/2019/3590623 |
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| author | Rahul Rajendran Kevan Iffrig Deepak K Pruthi Allison Wheeler Brian Neuman Dharam Kaushik Ahmed M Mansour Karen Panetta Sos Agaian Michael A. Liss |
| author_facet | Rahul Rajendran Kevan Iffrig Deepak K Pruthi Allison Wheeler Brian Neuman Dharam Kaushik Ahmed M Mansour Karen Panetta Sos Agaian Michael A. Liss |
| author_sort | Rahul Rajendran |
| collection | DOAJ |
| description | Objective. To develop software to assess the potential aggressiveness of an incidentally detected renal mass using images. Methods. Thirty randomly selected patients who underwent nephrectomy for renal cell carcinoma (RCC) had their images independently reviewed by engineers. Tumor “Roughness” was based on image algorithm of tumor topographic features visualized on computed tomography (CT) scans. Univariant and multivariant statistical analyses are utilized for analysis. Results. We investigated 30 subjects that underwent partial or radical nephrectomy. After excluding poor image-rendered images, 27 patients remained (benign cyst = 1, oncocytoma = 2, clear cell RCC = 15, papillary RCC = 7, and chromophobe RCC = 2). The mean roughness score for each mass is 1.18, 1.16, 1.27, 1.52, and 1.56 units, respectively (p<0.004). Renal masses were correlated with tumor roughness (Pearson’s, p=0.02). However, tumor size itself was larger in benign tumors (p=0.1). Linear regression analysis noted that the roughness score is the most influential on the model with all other demographics being equal including tumor size (p=0.003). Conclusion. Using basic CT imaging software, tumor topography (“roughness”) can be quantified and correlated with histologies such as RCC subtype and could lead to determining aggressiveness of small renal masses. |
| format | Article |
| id | doaj-art-ae3037d6d6e14525ade828327d89b02a |
| institution | Kabale University |
| issn | 1687-6369 1687-6377 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Urology |
| spelling | doaj-art-ae3037d6d6e14525ade828327d89b02a2025-02-03T01:26:44ZengWileyAdvances in Urology1687-63691687-63772019-01-01201910.1155/2019/35906233590623Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer SubtypesRahul Rajendran0Kevan Iffrig1Deepak K Pruthi2Allison Wheeler3Brian Neuman4Dharam Kaushik5Ahmed M Mansour6Karen Panetta7Sos Agaian8Michael A. Liss9University of Texas San Antonio, Department of Electrical and Computer Engineering, San Antonio, TX, USAUniversity of Texas Health San Antonio, Department of Urology, San Antonio, TX, USAUniversity of Texas Health San Antonio, Department of Urology, San Antonio, TX, USAUniversity of Texas Health San Antonio, Department of Urology, San Antonio, TX, USAUniversity of Texas Health San Antonio, Department of Urology, San Antonio, TX, USAUniversity of Texas Health San Antonio, Department of Urology, San Antonio, TX, USAUniversity of Texas Health San Antonio, Department of Urology, San Antonio, TX, USATufts University, School of Engineering, Medford, MA, USACollege of Staten Island, The City University of New York (CSI-CUNY), New York City, NY, USAUniversity of Texas Health San Antonio, Department of Urology, San Antonio, TX, USAObjective. To develop software to assess the potential aggressiveness of an incidentally detected renal mass using images. Methods. Thirty randomly selected patients who underwent nephrectomy for renal cell carcinoma (RCC) had their images independently reviewed by engineers. Tumor “Roughness” was based on image algorithm of tumor topographic features visualized on computed tomography (CT) scans. Univariant and multivariant statistical analyses are utilized for analysis. Results. We investigated 30 subjects that underwent partial or radical nephrectomy. After excluding poor image-rendered images, 27 patients remained (benign cyst = 1, oncocytoma = 2, clear cell RCC = 15, papillary RCC = 7, and chromophobe RCC = 2). The mean roughness score for each mass is 1.18, 1.16, 1.27, 1.52, and 1.56 units, respectively (p<0.004). Renal masses were correlated with tumor roughness (Pearson’s, p=0.02). However, tumor size itself was larger in benign tumors (p=0.1). Linear regression analysis noted that the roughness score is the most influential on the model with all other demographics being equal including tumor size (p=0.003). Conclusion. Using basic CT imaging software, tumor topography (“roughness”) can be quantified and correlated with histologies such as RCC subtype and could lead to determining aggressiveness of small renal masses.http://dx.doi.org/10.1155/2019/3590623 |
| spellingShingle | Rahul Rajendran Kevan Iffrig Deepak K Pruthi Allison Wheeler Brian Neuman Dharam Kaushik Ahmed M Mansour Karen Panetta Sos Agaian Michael A. Liss Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes Advances in Urology |
| title | Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes |
| title_full | Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes |
| title_fullStr | Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes |
| title_full_unstemmed | Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes |
| title_short | Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes |
| title_sort | initial evaluation of computer assisted radiologic assessment for renal mass edge detection as an indication of tumor roughness to predict renal cancer subtypes |
| url | http://dx.doi.org/10.1155/2019/3590623 |
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