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37 results on '"Bontempi, Dennis"'

4. Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

Author

Krishnaswamy, Deepa, Bontempi, Dennis, Thiriveedhi, Vamsi, Punzo, Davide, Clunie, David, Bridge, Christopher P, Aerts, Hugo JWL, Kikinis, Ron, and Fedorov, Andrey

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging.

Published
2023

6. CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Author

Bontempi, Dennis, Benini, Sergio, Signoroni, Alberto, Svanera, Michele, and Muckli, Lars

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

Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Recently, methods deploying solutions based on Convolutional Neural Networks (CNNs) are making the direct analysis of out-of-the-scanner data feasible. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This entails a loss of global contextual information thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed by 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements clearly prefer our solution with respect to the widely adopted atlas-based FreeSurfer software., Comment: 15 pages, 6 figures

Published
2019

7. Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial

Author

Kann, Benjamin H, Likitlersuang, Jirapat, Bontempi, Dennis, Ye, Zezhong, Aneja, Sanjay, Bakst, Richard, Kelly, Hillary R, Juliano, Amy F, Payabvash, Sam, Guenette, Jeffrey P, Uppaluri, Ravindra, Margalit, Danielle N, Schoenfeld, Jonathan D, Tishler, Roy B, Haddad, Robert, Aerts, Hugo J W L, Garcia, Joaquin J, Flamand, Yael, Subramaniam, Rathan M, Burtness, Barbara A, and Ferris, Robert L

Published
2023
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8. Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors

Author

Tohidinezhad, Fariba, Bontempi, Dennis, Zhang, Zhen, Dingemans, Anne-Marie, Aerts, Joachim, Bootsma, Gerben, Vansteenkiste, Johan, Hashemi, Sayed, Smit, Egbert, Gietema, Hester, Aerts, Hugo JWL., Dekker, Andre, Hendriks, Lizza E.L., Traverso, Alberto, and De Ruysscher, Dirk

Published
2023
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9. Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge

Author

Rao, Chinmay, Pai, Suraj, Hadzic, Ibrahim, Zhovannik, Ivan, Bontempi, Dennis, Dekker, Andre, Teuwen, Jonas, Traverso, Alberto, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, and Depeursinge, Adrien, editor

Published
2021
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11. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence

Author

Fedorov, Andrey, primary, Longabaugh, William J. R., additional, Pot, David, additional, Clunie, David A., additional, Pieper, Steven D., additional, Gibbs, David L., additional, Bridge, Christopher, additional, Herrmann, Markus D., additional, Homeyer, André, additional, Lewis, Rob, additional, Aerts, Hugo J. W. L., additional, Krishnaswamy, Deepa, additional, Thiriveedhi, Vamsi Krishna, additional, Ciausu, Cosmin, additional, Schacherer, Daniela P., additional, Bontempi, Dennis, additional, Pihl, Todd, additional, Wagner, Ulrike, additional, Farahani, Keyvan, additional, Kim, Erika, additional, and Kikinis, Ron, additional

Published
2023
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13. Decoding biological age from face photographs using deep learning

Author

Zalay, Osbert, primary, Bontempi, Dennis, additional, Bitterman, Danielle S, additional, Birkbak, Nicolai, additional, Shyr, Derek, additional, Haugg, Fridolin, additional, Qian, Jack M, additional, Roberts, Hannah, additional, Perni, Subha, additional, Prudente, Vasco, additional, Pai, Suraj, additional, Dekker, Andre, additional, Haibe-Kains, Benjamin, additional, Guthier, Christian, additional, Balboni, Tracy, additional, Warren, Laura, additional, Krishan, Monica, additional, Kann, Benjamin H, additional, Swanton, Charles, additional, Ruysscher, Dirk De, additional, Mak, Raymond H, additional, and Aerts, Hugo JWL, additional

Published
2023
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14. Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging

Author

Pai, Suraj, primary, Bontempi, Dennis, additional, Hadzic, Ibrahim, additional, Prudente, Vasco, additional, Sokač, Mateo, additional, Chaunzwa, Tafadzwa L., additional, Bernatz, Simon, additional, Hosny, Ahmed, additional, Mak, Raymond H, additional, Birkbak, Nicolai J, additional, and Aerts, Hugo JWL, additional

Published
2023
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17. Supplemental video 2 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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18. Supplemental video 1 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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19. Supplemental video 4 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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20. Supplemental video 3 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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21. Data from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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23. Segmentation Uncertainty Estimation as a Sanity Check for Image Biomarker Studies

Author

Zhovannik, I., Bontempi, Dennis, Romita, Alessio, Pfaehler, E., Primakov, Sergey, Dekker, Andre, Bussink, J., Traverso, Alberto, Monshouwer, R., Zhovannik, I., Bontempi, Dennis, Romita, Alessio, Pfaehler, E., Primakov, Sergey, Dekker, Andre, Bussink, J., Traverso, Alberto, and Monshouwer, R.

Abstract

Contains fulltext : 248180.pdf (Publisher’s version ) (Open Access)

Published
2022

27. NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., additional, Pot, David, additional, Clunie, David A., additional, Pieper, Steve, additional, Aerts, Hugo J.W.L., additional, Homeyer, André, additional, Lewis, Rob, additional, Akbarzadeh, Afshin, additional, Bontempi, Dennis, additional, Clifford, William, additional, Herrmann, Markus D., additional, Höfener, Henning, additional, Octaviano, Igor, additional, Osborne, Chad, additional, Paquette, Suzanne, additional, Petts, James, additional, Punzo, Davide, additional, Reyes, Madelyn, additional, Schacherer, Daniela P., additional, Tian, Mi, additional, White, George, additional, Ziegler, Erik, additional, Shmulevich, Ilya, additional, Pihl, Todd, additional, Wagner, Ulrike, additional, Farahani, Keyvan, additional, and Kikinis, Ron, additional

Published
2021
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36. Decoding biological age from face photographs using deep learning.

Author

Zalay O, Bontempi D, Bitterman DS, Birkbak N, Shyr D, Haugg F, Qian JM, Roberts H, Perni S, Prudente V, Pai S, Dekker A, Haibe-Kains B, Guthier C, Balboni T, Warren L, Krishan M, Kann BH, Swanton C, Ruysscher D, Mak RH, and Aerts HJ

Abstract

Because humans age at different rates, a person's physical appearance may yield insights into their biological age and physiological health more reliably than their chronological age. In medicine, however, appearance is incorporated into medical judgments in a subjective and non-standardized fashion. In this study, we developed and validated FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs. FaceAge was trained on data from 58,851 healthy individuals, and clinical utility was evaluated on data from 6,196 patients with cancer diagnoses from two institutions in the United States and The Netherlands. To assess the prognostic relevance of FaceAge estimation, we performed Kaplan Meier survival analysis. To test a relevant clinical application of FaceAge, we assessed the performance of FaceAge in end-of-life patients with metastatic cancer who received palliative treatment by incorporating FaceAge into clinical prediction models. We found that, on average, cancer patients look older than their chronological age, and looking older is correlated with worse overall survival. FaceAge demonstrated significant independent prognostic performance in a range of cancer types and stages. We found that FaceAge can improve physicians' survival predictions in incurable patients receiving palliative treatments, highlighting the clinical utility of the algorithm to support end-of-life decision-making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, while age was not. These findings may extend to diseases beyond cancer, motivating using deep learning algorithms to translate a patient's visual appearance into objective, quantitative, and clinically useful measures.

Published
2023
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37. Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging.

Author

Pai S, Bontempi D, Prudente V, Hadzic I, Sokač M, Chaunzwa TL, Bernatz S, Hosny A, Mak RH, Birkbak NJ, and Aerts HJ

Abstract

Foundation models represent a recent paradigm shift in deep learning, where a single large-scale model trained on vast amounts of data can serve as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labeled datasets are often scarce. Here, we developed a foundation model for imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of imaging-based biomarkers. We found that they facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed their conventional supervised counterparts on downstream tasks. The performance gain was most prominent when training dataset sizes were very limited. Furthermore, foundation models were more stable to input and inter-reader variations and showed stronger associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering novel imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings., Competing Interests: COMPETING INTERESTS The authors declare no competing interests.

Published
2023
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