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1. Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging

Author

Anari, Pouria Yazdian, Obiezu, Fiona, Lay, Nathan, Firouzabadi, Fatemeh Dehghani, Chaurasia, Aditi, Golagha, Mahshid, Singh, Shiva, Homayounieh, Fatemeh, Zahergivar, Aryan, Harmon, Stephanie, Turkbey, Evrim, Gautam, Rabindra, Ma, Kevin, Merino, Maria, Jones, Elizabeth C., Ball, Mark W., Linehan, W. Marston, Turkbey, Baris, and Malayeri, Ashkan A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Introduction This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results The primary training set showed an average PPV of 0.94 +/- 0.01, sensitivity of 0.87 +/- 0.04, and mAP of 0.91 +/- 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 +/- 0.03, sensitivity of 0.98 +/- 0.004, and mAP of 0.95 +/- 0.01. Conclusion Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

Published
2024

2. Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images

Author

Anari, Pouria Yazdian, Lay, Nathan, Chaurasia, Aditi, Gopal, Nikhil, Samimi, Safa, Harmon, Stephanie, Gautam, Rabindra, Ma, Kevin, Firouzabadi, Fatemeh Dehghani, Turkbey, Evrim, Merino, Maria, Jones, Elizabeth C., Ball, Mark W., Linehan, W. Marston, Turkbey, Baris, and Malayeri, Ashkan A.

Subjects
Quantitative Biology - Quantitative Methods, Physics - Medical Physics
Abstract

We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.

Published
2023

6. Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation

Author

Herington, Jonathan, primary, McCradden, Melissa D., additional, Creel, Kathleen, additional, Boellaard, Ronald, additional, Jones, Elizabeth C., additional, Jha, Abhinav K., additional, Rahmim, Arman, additional, Scott, Peter J.H., additional, Sunderland, John J., additional, Wahl, Richard L., additional, Zuehlsdorff, Sven, additional, and Saboury, Babak, additional

Published
2023
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7. Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance

Author

Herington, Jonathan, primary, McCradden, Melissa D., additional, Creel, Kathleen, additional, Boellaard, Ronald, additional, Jones, Elizabeth C., additional, Jha, Abhinav K., additional, Rahmim, Arman, additional, Scott, Peter J.H., additional, Sunderland, John J., additional, Wahl, Richard L., additional, Zuehlsdorff, Sven, additional, and Saboury, Babak, additional

Published
2023
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10. A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging

Author

Ogunleye, Olanrewaju A., primary, Raviprakash, Harish, additional, Simmons, Ashlee M., additional, Bovell, Rhasaan T.M., additional, Martinez, Pedro E., additional, Yanovski, Jack A., additional, Berman, Karen F., additional, Schmidt, Peter J., additional, Jones, Elizabeth C., additional, Bagheri, Hadi, additional, Biassou, Nadia M., additional, and Hsu, Li-Yueh, additional

Published
2023
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16. Tumor Doubling Time Using CT Volumetric Segmentation in Metastatic Adrenocortical Carcinoma

Author

Fuller, Sarah N., primary, Shafiei, Ahmad, additional, Venzon, David J., additional, Liewehr, David J., additional, Mauda Havanuk, Michal, additional, Ilanchezhian, Maran G., additional, Edgerly, Maureen, additional, Anderson, Victoria L., additional, Levy, Elliot B., additional, Hoang, Choung D., additional, Jones, Elizabeth C., additional, Reilly, Karlyne M., additional, Widemann, Brigitte C., additional, Wood, Bradford J., additional, Bagheri, Hadi, additional, and Del Rivero, Jaydira, additional

Published
2021
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17. Evaluation of Coronary Plaques and Stents with Conventional and Photon-counting CT: Benefits of High-Resolution Photon-counting CT

Author

Rajagopal, Jayasai R., primary, Farhadi, Faraz, additional, Richards, Taylor, additional, Nikpanah, Moozhan, additional, Sahbaee, Pooyan, additional, Shanbhag, Sujata M., additional, Bandettini, W. Patricia, additional, Saboury, Babak, additional, Malayeri, Ashkan A., additional, Pritchard, William F., additional, Jones, Elizabeth C., additional, Samei, Ehsan, additional, and Chen, Marcus Y., additional

Published
2021
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21. Imaging, Pathology, and Immune Correlates in the Woodchuck Hepatic Tumor Model

Author

Mauda-Havakuk, Michal, primary, Mikhail, Andrew S, additional, Starost, Matthew F, additional, Jones, Elizabeth C, additional, Karim, Baktiar, additional, Kleiner, David E, additional, Partanen, Ari, additional, Esparza-Trujillo, Juan A, additional, Bakhutashvili, Ivane, additional, Wakim, Paul G, additional, Kassin, Michael T, additional, Lewis, Andrew L, additional, Karanian, John W, additional, Wood, Bradford J, additional, and Pritchard, William F, additional

Published
2021
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22. The incidence and natural history of osteonecrosis in HIV-infected adults

Author

Morse, Caryn G., Mican, JoAnn M., Jones, Elizabeth C., Joe, Galen O., Rick, Margaret E., Formentini, Elizabeth, and Kovacs, Joseph A.

Subjects
HIV patients -- Research, HIV patients -- Health aspects, HIV (Viruses) -- Research, HIV (Viruses) -- Health aspects, HIV (Viruses) -- Development and progression, Bones -- Necrosis, Bones -- Research, Bones -- Risk factors, Bones -- Health aspects, Health, Health care industry
Published
2007

29. Development of Hepatic Steatosis After Chemotherapy for Non-Hodgkin Lymphoma

Author

Ben-Yakov, Gil, primary, Alao, Hawwa, additional, Haydek, John P., additional, Fryzek, Nancy, additional, Cho, Min Ho, additional, Hemmati, Mehdi, additional, Samala, Vikram, additional, Shovlin, Margaret, additional, Dunleavy, Kieron, additional, Wilson, Wyndham, additional, Jones, Elizabeth C., additional, and Rotman, Yaron, additional

Published
2018
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30. Development of Hepatic Steatosis After Chemotherapy for Non‐Hodgkin Lymphoma.

Author

Ben‐Yakov, Gil, Alao, Hawwa, Haydek, John P., Fryzek, Nancy, Cho, Min Ho, Hemmati, Mehdi, Samala, Vikram, Shovlin, Margaret, Dunleavy, Kieron, Wilson, Wyndham, Jones, Elizabeth C., and Rotman, Yaron

Subjects
FATTY degeneration, LYMPHOMAS, CANCER chemotherapy, RITUXIMAB, CYCLOPHOSPHAMIDE, DOXORUBICIN, PREDNISONE
Abstract

Nonalcoholic fatty liver disease is the most common liver disorder in the developed world. Although typically reflecting caloric overload, it can also be secondary to drug toxicity. We aimed to describe the incidence and risk factors for de novo steatosis during chemotherapy for non‐Hodgkin lymphoma (NHL). In this retrospective case‐control study, adult patients with NHL were treated with rituximab, cyclophosphamide, doxorubicin, prednisone, and vincristine (R‐CHOP) or R‐CHOP + etoposide (EPOCH‐R). Patients with liver disease or steatosis were excluded. Abdominal computed tomography was performed pretreatment and at 3‐ to 6‐month intervals and reviewed for steatosis. Patients with de novo steatosis were matched 1:1 to controls by age, sex, and ethnicity. Of 251 treated patients (median follow‐up 53 months), 25 (10%) developed de novo steatosis, with the vast majority (23 of 25; 92%) developing it after chemotherapy. Of those, 14 (61%) developed steatosis within the first 18 months posttreatment and 20 (87%) within 36 months. Cases had higher baseline body mass index (BMI; mean ± SD, 29.0 ± 6.5 versus 26.0 ± 5.2 kg/m2; P = 0.014) and hyperlipidemia (12% versus 2%; P = 0.035). Although their weights did not change during chemotherapy, BMI in cases increased by 2.4 ± 2 kg/m2 (mean ± SD) from end of treatment to steatosis compared to 0.68 ± 1.4 in controls (P = 0.003). Etoposide‐containing regimens were associated with a shorter time to steatosis (median 34 weeks versus 154 weeks; P < 0.001) despite similar baseline risk factors. Conclusion: The recovery period from NHL chemotherapy appears to be a "hot spot" for development of fatty liver, driven by early posttreatment weight gain, especially in subjects with baseline risk factors. Our current study evaluates the development of liver steatosis after chemotherapy treatment for Non Hodgkin Lymphoma. This study suggests that the recovery period from treatment appears to be a "hot spot" for development of fatty liver, driven by early post‐treatment weight gain, especially in subjects with baseline risk factors and should be addressed in post remission follow up monitoring. [ABSTRACT FROM AUTHOR]

Published
2019
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31. Studies of Association between the Gene for Calpain-10 and Type 2 Diabetes Mellitus in the United Kingdom

Author

Evans, Julie C., Frayling, Timothy M., Cassell, Paul G., Saker, Philip J, Hitman, Graham A., Walker Mark, Levy, Jonathan C., O'Rahilly, Stephen, Subba Rao, Pamidighantam V., Bennett, Amanda J., Jones, Elizabeth C., Menzel, Stephan, Prestwich, Philip, Simecek, Nikol, Wishart, Marie, Dhillon, Ranjit, Fletcher, Chris, Millward, Ann, Demaine, Andrew, Wilkin, Terence, Horikowa, Yukio, Cox, Nancy J., Bell, Graeme I., Ellard, Sian, McCarthy, Mark I., and Hattersley, Andrew T.

Subjects
Linkage (Genetics) -- Analysis, Calpain -- Genetic aspects, Type 2 diabetes -- Genetic aspects, Population genetics -- Demographic aspects, Biological sciences
Published
2001

32. Complications after Nephron-sparing Interventions for Renal Tumors: Imaging Findings and Management

Author

Chaurasia, Aditi, Singh, Shiva, Homayounieh, Fatemeh, Gopal, Nikhil, Jones, Elizabeth C., Linehan, W. Marston, Shyn, Paul B., Ball, Mark W., and Malayeri, Ashkan A.

Abstract

Radiologists’ knowledge of the postoperative imaging characteristics of the most common complications of nephron-sparing interventions, such as hemorrhage, pseudoaneurysm, arteriovenous fistula, or urine leak, is crucial in the diagnostic management of renal tumors.

Published
2023
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33. Assessing Splenomegaly

Author

Linguraru, Marius George, primary, Sandberg, Jesse K., additional, Jones, Elizabeth C., additional, and Summers, Ronald M., additional

Published
2013
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34. Assessing Hepatomegaly

Author

Linguraru, Marius George, primary, Sandberg, Jesse K., additional, Jones, Elizabeth C., additional, Petrick, Nicholas, additional, and Summers, Ronald M., additional

Published
2012
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35. A Pilot Study of MUC-1/CEA/TRICOM Poxviral-Based Vaccine in Patients with Metastatic Breast and Ovarian Cancer

Author

Mohebtash, Mahsa, primary, Tsang, Kwong-Yok, additional, Madan, Ravi A., additional, Huen, Ngar-Yee, additional, Poole, Diane J., additional, Jochems, Caroline, additional, Jones, Jacquin, additional, Ferrara, Theresa, additional, Heery, Christopher R., additional, Arlen, Philip M., additional, Steinberg, Seth M., additional, Pazdur, Mary, additional, Rauckhorst, Myrna, additional, Jones, Elizabeth C., additional, Dahut, William L., additional, Schlom, Jeffrey, additional, and Gulley, James L., additional

Published
2011
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38. Evaluation of the Aortic Root by MRI

Author

Summers, Ronald M., primary, Andrasko-Bourgeois, Jonathan, additional, Feuerstein, Irwin M., additional, Hill, Suvimol C., additional, Jones, Elizabeth C., additional, Busse, Mary K., additional, Wise, Betty, additional, Bove, Karen E., additional, Rishforth, Beth A., additional, Tucker, Eben, additional, Spray, Thomas L., additional, and Hoeg, Jeffrey M., additional

Published
1998
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39. Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.

Author

Anari PY, Obiezu F, Lay N, Firouzabadi FD, Chaurasia A, Golagha M, Singh S, Homayounieh F, Zahergivar A, Harmon S, Turkbey E, Gautam R, Ma K, Merino M, Jones EC, Ball MW, Marston Linehan W, Turkbey B, and Malayeri AA

Abstract

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats., Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP)., Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01., Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

Published
2024

40. Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images.

Author

Anari PY, Lay N, Chaurasia A, Gopal N, Samimi S, Harmon S, Gautam R, Ma K, Firouzabadi FD, Turkbey E, Merino M, Jones EC, Ball MW, Linehan WM, Turkbey B, and Malayeri AA

Abstract

We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.

Published
2023

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