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1. Vision Foundation Models for Computed Tomography

Author

Pai, Suraj, Hadzic, Ibrahim, Bontempi, Dennis, Bressem, Keno, Kann, Benjamin H., Fedorov, Andriy, Mak, Raymond H., and Aerts, Hugo J. W. L.

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

Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (CT) scans from the Imaging Data Commons through label-agnostic contrastive learning. We evaluated CT-FM across four categories of tasks, namely, whole-body and tumor segmentation, head CT triage, medical image retrieval, and semantic understanding, showing superior performance against state-of-the-art models. Beyond quantitative success, CT-FM demonstrated the ability to cluster regions anatomically and identify similar anatomical and structural concepts across scans. Furthermore, it remained robust across test-retest settings and indicated reasonable salient regions attached to its embeddings. This study demonstrates the value of large-scale medical imaging foundation models and by open-sourcing the model weights, code, and data, aims to support more adaptable, reliable, and interpretable AI solutions in radiology., Comment: 6 figures, followed by 9 Extended Data Figures and a Supplementary Information document

Published
2025

2. Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy

Author

Zha, Yining, Kann, Benjamin H., Ye, Zezhong, Zapaishchykova, Anna, He, John, Hsu, Shu-Hui, Leeman, Jonathan E., Fitzgerald, Kelly J., Kozono, David E., Mak, Raymond H., and Aerts, Hugo J. W. L.

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

Introduction: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics. Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes. Methods: A retrospective study of 47 MR-guided lung SBRT treatments for 39 patients was conducted. Radiomic features were extracted using Pyradiomics, and stability was evaluated temporally and spatially. Delta radiomics were correlated with radiation dose delivery and assessed for associations with tumor control and survival with Cox regressions. Results: Among 107 features, 49 demonstrated temporal stability, and 57 showed spatial stability. Fifteen stable and non-collinear features were analyzed. Median Skewness and surface to volume ratio decreased with radiation dose fraction delivery, while coarseness and 90th percentile values increased. Skewness had the largest relative median absolute changes (22%-45%) per fraction from baseline and was associated with locoregional failure (p=0.012) by analysis of covariance. Skewness, Elongation, and Flatness were significantly associated with local recurrence-free survival, while tumor diameter and volume were not. Conclusions: Our study establishes the feasibility and stability of delta radiomics analysis for MR-guided lung SBRT. Findings suggest that MR delta radiomics can capture short-term radiographic manifestations of intra-tumoral radiation effect.

Published
2024

3. The impact of using an AI chatbot to respond to patient messages

Author

Chen, Shan, Guevara, Marco, Moningi, Shalini, Hoebers, Frank, Elhalawani, Hesham, Kann, Benjamin H., Chipidza, Fallon E., Leeman, Jonathan, Aerts, Hugo J. W. L., Miller, Timothy, Savova, Guergana K., Mak, Raymond H., Lustberg, Maryam, Afshar, Majid, and Bitterman, Danielle S.

Subjects
Computer Science - Computation and Language
Abstract

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation., Comment: 4 figures and tables in main, submitted for review

Published
2023

4. Large Language Models to Identify Social Determinants of Health in Electronic Health Records

Author

Guevara, Marco, Chen, Shan, Thomas, Spencer, Chaunzwa, Tafadzwa L., Franco, Idalid, Kann, Benjamin, Moningi, Shalini, Qian, Jack, Goldstein, Madeleine, Harper, Susan, Aerts, Hugo JWL, Savova, Guergana K., Mak, Raymond H., and Bitterman, Danielle S.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Social determinants of health (SDoH) have an important impact on patient outcomes but are incompletely collected from the electronic health records (EHR). This study researched the ability of large language models to extract SDoH from free text in EHRs, where they are most commonly documented, and explored the role of synthetic clinical text for improving the extraction of these scarcely documented, yet extremely valuable, clinical data. 800 patient notes were annotated for SDoH categories, and several transformer-based models were evaluated. The study also experimented with synthetic data generation and assessed for algorithmic bias. Our best-performing models were fine-tuned Flan-T5 XL (macro-F1 0.71) for any SDoH, and Flan-T5 XXL (macro-F1 0.70). The benefit of augmenting fine-tuning with synthetic data varied across model architecture and size, with smaller Flan-T5 models (base and large) showing the greatest improvements in performance (delta F1 +0.12 to +0.23). Model performance was similar on the in-hospital system dataset but worse on the MIMIC-III dataset. Our best-performing fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models for both tasks. These fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p<0.05). At the patient-level, our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. Our method can effectively extracted SDoH information from clinic notes, performing better compare to GPT zero- and few-shot settings. These models could enhance real-world evidence on SDoH and aid in identifying patients needing social support., Comment: Peer-reviewed version published at NPJ Digital Medicine: https://www.nature.com/articles/s41746-023-00970-0

Published
2023
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5. Developing and disseminating an electronic penicillin allergy de-labelling tool using the model for improvement framework

Author

Saravanabavan, Sujen, McKernan, Patrick, Cameron, Scott, Kwan, Natasha, Kang, Kristopher T., Roberts, Ashley, Carr, Roxane, Mak, Raymond, Elwood, Chelsea, Paquette, Vanessa, Stimpson, Rochelle, Abrahams, Bethina, Chan, Edmond S., Slayter, Kathryn, Rahier, Alicia, Sainchuk, Irina, Olsen, Sharla, Kucey, Melissa, Shamseddine, Jinan, Babiker, Zahir Osman Eltahir, and Wong, Tiffany

Published
2024
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10. Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy

Author

Chen, Shan, Guevara, Marco, Ramirez, Nicolas, Murray, Arpi, Warner, Jeremy L., Aerts, Hugo JWL, Miller, Timothy A., Savova, Guergana K., Mak, Raymond H., and Bitterman, Danielle S.

Subjects
Computer Science - Computation and Language
Abstract

Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT. We fine-tuned statistical and pre-trained BERT-based models for three esophagitis classification tasks: Task 1) presence of esophagitis, Task 2) severe esophagitis or not, and Task 3) no esophagitis vs. grade 1 vs. grade 2-3. Transferability was tested on 345 notes from patients with esophageal cancer undergoing RT. Fine-tuning PubmedBERT yielded the best performance. The best macro-F1 was 0.92, 0.82, and 0.74 for Task 1, 2, and 3, respectively. Selecting the most informative note sections during fine-tuning improved macro-F1 by over 2% for all tasks. Silver-labeled data improved the macro-F1 by over 3% across all tasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and 0.65 for Task 1, 2, and 3, respectively, without additional fine-tuning. To our knowledge, this is the first effort to automatically extract esophagitis toxicity severity according to CTCAE guidelines from clinic notes. The promising performance provides proof-of-concept for NLP-based automated detailed toxicity monitoring in expanded domains., Comment: 17 pages, 6 tables, 1figure, submiting to JCO-CCI for review

Published
2023
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11. Motion Management and Tracking

Author

Huynh, Elizabeth, Omari, Eenas A., Lim, Sara N., Mak, Raymond H., Das, Indra J., editor, Alongi, Filippo, editor, Yadav, Poonam, editor, and Mittal, Bharat B., editor

Published
2024
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13. Systematic Review of Sublobar Resection for Treatment of High-Risk Patients with Stage I Non-Small Cell Lung Cancer: The American Association for Thoracic Surgery Expert Consensus Document

Author

Merritt, Robert E., Brunelli, Alessandro, Walsh, Garrett, Murthy, Sudish, Schuchert, Matthew J., Varghese, Thomas K., Lanuti, Michael, Wolf, Andrea, Keshavarz, Homa, Loo, Billy W., Suh, Robert D., Mak, Raymond H., Criner, Gerard J., Mazzone, Peter J., Liptay, Michael, Wafford, Q. Eileen, Marshall, M. Blair, Tong, Betty, Pettiford, Brian, Rocco, Gaetano, Luketich, James, D’Amico, Thomas A., Swanson, Scott J., and Pennathur, Arjun

Published
2025
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14. Systematic Review of Image-Guided Thermal Ablation for Treatment of High-Risk Patients with Stage I Non-Small Cell Lung Cancer: The American Association for Thoracic Surgery Expert Consensus Document

Author

Lanuti, Michael, Suh, Robert D., Criner, Gerard J., Mazzone, Peter J., Marshall, M. Blair, Tong, Betty, Merritt, Robert E., Wolf, Andrea, Keshavarz, Homa, Loo, Billy W., Mak, Raymond H., Brunelli, Alessandro, Walsh, Garrett, Liptay, Michael, Wafford, Q. Eileen, Murthy, Sudish, Pettiford, Brian, Rocco, Gaetano, Luketich, James, Schuchert, Matthew J., Varghese, Thomas K., D’Amico, Thomas A., Swanson, Scott J., and Pennathur, Arjun

Published
2025
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15. Systematic Review of Stereotactic Ablative Radiotherapy (SABR)/Stereotactic Body Radiation Therapy (SBRT) for Treatment of High-Risk Patients with Stage I Non-Small Cell Lung Cancer: The American Association of Thoracic Surgery Expert Consensus Document

Author

Wolf, Andrea, Loo, Billy W., Mak, Raymond H., Liptay, Michael, Pettiford, Brian, Rocco, Gaetano, Lanuti, Michael, Merritt, Robert E., Keshavarz, Homa, Suh, Robert D., Brunelli, Alessandro, Criner, Gerard J., Mazzone, Peter J., Walsh, Garrett, Wafford, Q. Eileen, Murthy, Sudish, Marshall, M. Blair, Tong, Betty, Luketich, James, Schuchert, Matthew J., Varghese, Thomas K., D’Amico, Thomas A., Pennathur, Arjun, and Swanson, Scott J.

Published
2025
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16. Systematic Review of the Comparative Studies of Image-Guided Thermal Ablation, Stereotactic Radiosurgery, and Sublobar Resection for Treatment of High-Risk Patients with Stage I Non-Small Cell Lung Cancer: The American Association for Thoracic Surgery Expert Consensus Document

Author

Pennathur, Arjun, Lanuti, Michael, Merritt, Robert E., Wolf, Andrea, Keshavarz, Homa, Loo, Billy W., Suh, Robert D., Mak, Raymond H., Brunelli, Alessandro, Criner, Gerard J., Mazzone, Peter J., Walsh, Garrett, Liptay, Michael, Wafford, Q. Eileen, Murthy, Sudish, Marshall, M. Blair, Tong, Betty, Pettiford, Brian, Rocco, Gaetano, Luketich, James, Schuchert, Matthew J., Varghese, Thomas K., D’Amico, Thomas A., and Swanson, Scott J.

Published
2025
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19. Abstract 4137636: Cardiac radiation dose and changes in physical activity and quality of life in patients with locally advanced lung cancer

Author

Ko, Kyunga, Yegya-Raman, Nikhil, Han, Ivy, Mitchell, Joshua, Zou, Wei, Levin, William, Barrett, Leanne, Bravo, Paco, DI CARLI, MARCELO, Cohen, Roger, Hutton, Sandra, Jabbour, Salma, Jung, Wonyoung, Keltz, Jonathan, Mak, Raymond, Nagda, Suneel, Ohri, Nitin, Robinson, Clifford, Smith, Amanda, Sun, Lova, Soike, Michael, Walls, Gerard, Wang, Jessica, Feigenberg, Steve, and Ky, Bonnie

Published
2024
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20. Deep learning-based detection of intravenous contrast in computed tomography scans

Author

Ye, Zezhong, Qian, Jack M., Hosny, Ahmed, Zeleznik, Roman, Plana, Deborah, Likitlersuang, Jirapat, Zhang, Zhongyi, Mak, Raymond H., Aerts, Hugo J. W. L., and Kann, Benjamin H.

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

Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) and lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans manually annotated for contrast presence by clinical experts. Five different DL models were adopted and trained in HN training datasets for slice-level contrast detection. Model performances were evaluated on a hold-out set and on an independent validation set from another institution. DL models was then fine-tuned on chest CT data and externally validated on a separate chest CT dataset. Results: Initial DICOM metadata tags for IV contrast were missing or erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the best overall detection performance. For HN scans, AUC was 0.996 in the internal validation set (n = 216) and 1.0 in the external validation set (n = 595). The fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation set (n = 53), and AUC: 0.980 for the external validation set (n = 402). Conclusion: The DL model could accurately detect IV contrast in both HN and chest CT scans with near-perfect performance.

Published
2021

24. Durvalumab plus tremelimumab alone or in combination with low-dose or hypofractionated radiotherapy in metastatic non-small-cell lung cancer refractory to previous PD(L)-1 therapy: an open-label, multicentre, randomised, phase 2 trial

Author

Schoenfeld, Jonathan D, Giobbie-Hurder, Anita, Ranasinghe, Srinika, Kao, Katrina Z, Lako, Ana, Tsuji, Junko, Liu, Yang, Brennick, Ryan C, Gentzler, Ryan D, Lee, Carrie, Hubbard, Joleen, Arnold, Susanne M, Abbruzzese, James L, Jabbour, Salma K, Uboha, Nataliya V, Stephans, Kevin L, Johnson, Jennifer M, Park, Haeseong, Villaruz, Liza C, Sharon, Elad, Streicher, Howard, Ahmed, Mansoor M, Lyon, Hayley, Cibuskis, Carrie, Lennon, Niall, Jhaveri, Aashna, Yang, Lin, Altreuter, Jennifer, Gunasti, Lauren, Weirather, Jason L, Mak, Raymond H, Awad, Mark M, Rodig, Scott J, Chen, Helen X, Wu, Catherine J, Monjazeb, Arta M, and Hodi, F Stephen

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Clinical Research, Lung, Lung Cancer, Clinical Trials and Supportive Activities, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, 6.5 Radiotherapy and other non-invasive therapies, Aged, Antibodies, Monoclonal, Antibodies, Monoclonal, Humanized, Antineoplastic Combined Chemotherapy Protocols, Carcinoma, Non-Small-Cell Lung, Combined Modality Therapy, Female, Humans, Immune Checkpoint Inhibitors, Lung Neoplasms, Male, Middle Aged, Neoplasm Metastasis, Radiation Dose Hypofractionation, Radiotherapy Dosage, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundPatients with non-small-cell lung cancer (NSCLC) that is resistant to PD-1 and PD-L1 (PD[L]-1)-targeted therapy have poor outcomes. Studies suggest that radiotherapy could enhance antitumour immunity. Therefore, we investigated the potential benefit of PD-L1 (durvalumab) and CTLA-4 (tremelimumab) inhibition alone or combined with radiotherapy.MethodsThis open-label, multicentre, randomised, phase 2 trial was done by the National Cancer Institute Experimental Therapeutics Clinical Trials Network at 18 US sites. Patients aged 18 years or older with metastatic NSCLC, an Eastern Cooperative Oncology Group performance status of 0 or 1, and progression during previous PD(L)-1 therapy were eligible. They were randomly assigned (1:1:1) in a web-based system by the study statistician using a permuted block scheme (block sizes of three or six) without stratification to receive either durvalumab (1500 mg intravenously every 4 weeks for a maximum of 13 cycles) plus tremelimumab (75 mg intravenously every 4 weeks for a maximum of four cycles) alone or with low-dose (0·5 Gy delivered twice per day, repeated for 2 days during each of the first four cycles of therapy) or hypofractionated radiotherapy (24 Gy total delivered over three 8-Gy fractions during the first cycle only), 1 week after initial durvalumab-tremelimumab administration. Study treatment was continued until 1 year or until progression. The primary endpoint was overall response rate (best locally assessed confirmed response of a partial or complete response) and, along with safety, was analysed in patients who received at least one dose of study therapy. The trial is registered with ClinicalTrials.gov, NCT02888743, and is now complete.FindingsBetween Aug 24, 2017, and March 29, 2019, 90 patients were enrolled and randomly assigned, of whom 78 (26 per group) were treated. This trial was stopped due to futility assessed in an interim analysis. At a median follow-up of 12·4 months (IQR 7·8-15·1), there were no differences in overall response rates between the durvalumab-tremelimumab alone group (three [11·5%, 90% CI 1·2-21·8] of 26 patients) and the low-dose radiotherapy group (two [7·7%, 0·0-16·3] of 26 patients; p=0·64) or the hypofractionated radiotherapy group (three [11·5%, 1·2-21·8] of 26 patients; p=0·99). The most common grade 3-4 adverse events were dyspnoea (two [8%] in the durvalumab-tremelimumab alone group; three [12%] in the low-dose radiotherapy group; and three [12%] in the hypofractionated radiotherapy group) and hyponatraemia (one [4%] in the durvalumab-tremelimumab alone group vs two [8%] in the low-dose radiotherapy group vs three [12%] in the hypofractionated radiotherapy group). Treatment-related serious adverse events occurred in one (4%) patient in the durvalumab-tremelimumab alone group (maculopapular rash), five (19%) patients in the low-dose radiotherapy group (abdominal pain, diarrhoea, dyspnoea, hypokalemia, and respiratory failure), and four (15%) patients in the hypofractionated group (adrenal insufficiency, colitis, diarrhoea, and hyponatremia). In the low-dose radiotherapy group, there was one death from respiratory failure potentially related to study therapy.InterpretationRadiotherapy did not increase responses to combined PD-L1 plus CTLA-4 inhibition in patients with NSCLC resistant to PD(L)-1 therapy. However, PD-L1 plus CTLA-4 therapy could be a treatment option for some patients. Future studies should refine predictive biomarkers in this setting.FundingThe US National Institutes of Health and the Dana-Farber Cancer Institute.

Published
2022

25. Consolidation Osimertinib Versus Durvalumab Versus Observation After Concurrent Chemoradiation in Unresectable EGFR-Mutant NSCLC: A Multicenter Retrospective Cohort Study

Author

Nassar, Amin H., Kim, So Yeon, Aredo, Jacqueline V., Feng, Jamie, Shepherd, Frances, Xu, Chao, Kaldas, David, Gray, Jhanelle E., Dilling, Thomas J., Neal, Joel W., Wakelee, Heather A., Liu, Yufei, Lin, Steven H., Abuali, Tariq, Amini, Arya, Nie, Yunan, Patil, Tejas, Lobachov, Anastasiya, Bar, Jair, Fitzgerald, Bailey, Fujiwara, Yu, Marron, Thomas U., Thummalapalli, Rohit, Yu, Helena, Owen, Dwight H., Sharp, John, Farid, Saira, Rocha, Pedro, Arriola, Edurne, D’Aiello, Angelica, Cheng, Haiying, Whitaker, Ryan, Parikh, Kaushal, Ashara, Yash, Chen, Luxi, Sankar, Kamya, Harris, Jeremy P., Nagasaka, Misako, Ayanambakkam, Adanma, Velazquez, Ana I., Ragavan, Meera, Lin, Jessica J., Piotrowska, Zofia, Wilgucki, Molly, Reuss, Joshua, Luders, Heike, Grohe, Christian, Baena Espinar, Javier, Feiner, Ella, Punekar, Salman R., Gupta, Shruti, Leal, Ticiana, Kwiatkowski, David J., Mak, Raymond H., Adib, Elio, Naqash, Abdul Rafeh, and Goldberg, Sarah B.

Published
2024
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26. Surgical complications and clinical outcomes after dose-escalated trimodality therapy for non-small cell lung cancer in the era of intensity-modulated radiotherapy

Author

Liu, Kevin X, Sierra-Davidson, Kailan, Tyan, Kevin, Orlina, Lawrence T, Marcoux, J Paul, Kann, Benjamin H, Kozono, David E, Mak, Raymond H, White, Abby, and Singer, Lisa

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Lung Cancer, Clinical Research, Lung, Patient Safety, Evaluation of treatments and therapeutic interventions, 6.5 Radiotherapy and other non-invasive therapies, Aged, Carcinoma, Non-Small-Cell Lung, Chemoradiotherapy, Disease-Free Survival, Female, Humans, Lung Neoplasms, Radiotherapy Dosage, Radiotherapy, Intensity-Modulated, Retrospective Studies, Non-small cell lung cancer, Trimodality, Radiation, Intensity modulated radiotherapy, IMRT, Radiation dose, Other Physical Sciences, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis, Medical and biological physics
Abstract

BackgroundTrimodality therapy (TMT) with preoperative chemoradiation followed by surgical resection is used for locally-advanced non-small-cell lung cancer (LA-NSCLC). Traditionally, preoperative radiation doses ≤54 Gy are used due to concerns regarding excess morbidity, but little is known about outcomes and toxicities after TMT with intensity-modulated radiotherapy (IMRT) to higher doses.MethodsA retrospective analysis of patients who received planned TMT with IMRT for LA-NSCLC at Brigham and Women's Hospital/Dana-Farber Cancer Institute between 2008 and 2017 was performed. Clinical and treatment characteristics, pathologic response, and surgical toxicity were assessed. Kaplan-Meier method and log-rank test was used for survival outcomes. Cox proportional-hazards regression was used for multivariable analysis.ResultsForty-six patients received less than definitive doses of 7 days were associated with OS.ConclusionsWith IMRT, there was no increased rate of surgical complications in patients receiving higher doses of radiation. Survival outcomes or LOS did not differ based on radiation dose, but increased LOS was associated with worse OS. Larger prospective studies are needed to further examine outcomes after IMRT in patients with LA-NSCLC receiving TMT.

Published
2021

28. Body composition and lung cancer-associated cachexia in TRACERx

Author

Al-Sawaf, Othman, Weiss, Jakob, Skrzypski, Marcin, Lam, Jie Min, Karasaki, Takahiro, Zambrana, Francisco, Kidd, Andrew C., Frankell, Alexander M., Watkins, Thomas B. K., Martínez-Ruiz, Carlos, Puttick, Clare, Black, James R. M., Huebner, Ariana, Bakir, Maise Al, Sokač, Mateo, Collins, Susie, Veeriah, Selvaraju, Magno, Neil, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Ward, Sophia, Jayanth, Nick, Salgado, Roberto, Bridge, Christopher P., Christiani, David C., Mak, Raymond H., Bay, Camden, Rosenthal, Michael, Sattar, Naveed, Welsh, Paul, Liu, Ying, Perrimon, Norbert, Popuri, Karteek, Beg, Mirza Faisal, McGranahan, Nicholas, Hackshaw, Allan, Breen, Danna M., O’Rahilly, Stephen, Birkbak, Nicolai J., Aerts, Hugo J. W. L., Jamal-Hanjani, Mariam, and Swanton, Charles

Published
2023
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30. Automated temporalis muscle quantification and growth charts for children through adulthood

Author

Zapaishchykova, Anna, Liu, Kevin X., Saraf, Anurag, Ye, Zezhong, Catalano, Paul J., Benitez, Viviana, Ravipati, Yashwanth, Jain, Arnav, Huang, Julia, Hayat, Hasaan, Likitlersuang, Jirapat, Vajapeyam, Sridhar, Chopra, Rishi B., Familiar, Ariana M., Nabavidazeh, Ali, Mak, Raymond H., Resnick, Adam C., Mueller, Sabine, Cooney, Tabitha M., Haas-Kogan, Daphne A., Poussaint, Tina Y., Aerts, Hugo J.W.L., and Kann, Benjamin H.

Published
2023
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34. Penicillin de-labelling in vancouver, British Columbia, Canada: comparison of approaches, outcomes and future directions

Author

Saravanabavan, Sujen, Aulakh, Amneet, Douglas, Josh, Elwood, Chelsea, Erdle, Stephanie, Grant, Jennifer, Kang, Kristopher T., Kwan, Natasha, Lacaria, Katie, Lau, Tim T. Y., Lee, Colin, Leung, Victor, Lin, Yu-Chen, Mah, Allison, Nguyen, Anne, Paquette, Vanessa, Roberts, Ashley, Watt, Melissa, Van Schalkwyk, Julie, Zhang, Bei Yuan, Mak, Raymond, and Wong, Tiffany

Published
2023
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35. A pulmonary vein atlas for radiotherapy planning

Author

Walls, Gerard M, McCann, Conor, Ball, Peter, Atkins, Katelyn M, Mak, Raymond H, Bedair, Ahmed, O'Hare, Jolyne, McAleese, Jonathan, Harrison, Claire, Tumelty, Karen A, Crockett, Cathryn, Black, Sarah-Louise, Nelson, Catherine, O'Connor, John, Hounsell, Alan R, McGarry, Conor K, Butterworth, Karl T, Cole, Aidan J, Jain, Suneil, and Hanna, Gerard G

Published
2023
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40. Patterns of utilization and clinical adoption of 0.35 Tesla MR-guided radiation therapy in the United States – Understanding the transition to adaptive, ultra-hypofractionated treatments

Author

Chuong, Michael D., Clark, Mary Ann, Henke, Lauren E., Kishan, Amar U., Portelance, Lorraine, Parikh, Parag J., Bassetti, Michael F., Nagar, Himanshu, Rosenberg, Stephen A., Mehta, Minesh P., Refaat, Tamer, Rineer, Justin M., Smith, Adam, Seung, Steven, Zaki, Bassem I., Fuss, Martin, and Mak, Raymond H.

Published
2023
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46. Feasibility of Left Anterior Descending Coronary Artery Sparing Radiation Therapy for Locally Advanced Lung Cancer.

Author

Zhang, Samuel C., Silos, Katrina D., Gasho, Jordan O., Peony, Olivia, Polishchuk, Tayisiya, Semaan, Latifeh, Stiehl, Bradley, Hakimian, Behrooz, Mirhadi, Amin, Kamrava, Mitchell, Guthier, Christian, Nikolova, Andriana, McKenzie, Elizabeth, Steers, Jennifer, Mak, Raymond H., and Atkins, Katelyn M.

Abstract

Efforts to mitigate radiation therapy (RT)-associated cardiotoxicity have focused on constraining mean heart dose. However, recent studies have shown greater predictive power with cardiac substructure dose metrics, such as the left anterior descending (LAD) coronary artery volume (V) receiving 15 Gy (V15Gy) ≥10%. Herein, we investigated the feasibility of LAD radiation sparing in contemporary intensity modulated RT (IMRT)/volumetric modulated arc therapy (VMAT) lung cancer plans. Single institution retrospective analysis of 54 patients with locally advanced lung cancer treated with thoracic RT was conducted between February 2018 and August 2021. After excluding 33 (5 = non-IMRT/VMAT or intentionally LAD-optimized; 28 = LAD V15Gy <10%), 21 plans with LAD V15Gy ≥10% were identified for LAD reoptimization with intent to meet LAD V15Gy <10% while maintaining meeting organ at risk (OAR) metrics and target coverage with original plan parameters. Dosimetric variables were compared using paired t tests. Most patients (57.1%, 12/21) were treated with definitive RT, 8 of 21 patients (38.1%) with postoperative RT, and 1 with neoadjuvant RT. The median prescribed RT dose was 60 Gy (range, 50.4-66 Gy) in 30 fractions (range, 28-33 fractions). LAD reoptimized plans (vs original) led to significant reductions in mean LAD V15Gy (39.4% ± 13.9% vs 9.4% ± 13.0%; P <.001) and mean LAD dose (12.9 Gy ± 4.6 Gy vs 7.6 Gy ± 2.8 Gy; P <.001). Most (85.7%; 18/21) LAD reoptimized plans achieved LAD V15Gy <10%. There were no statistically significant differences in overall lung, esophageal, or spinal cord dose metrics. Only 1 reoptimization (1/21) exceeded an OAR constraint that was initially met in the original plan. To our knowledge, this is the first report describing the feasibility of LAD-optimized lung cancer RT planning using the newly identified LAD V15Gy constraint. We observed that LAD V15Gy <10% is achievable in more than 85% of plans initially exceeding this constraint, with minimal dosimetric tradeoffs. Our results support the feasibility of routine incorporation of the LAD as an OAR in modern thoracic IMRT/VMAT planning. [ABSTRACT FROM AUTHOR]

Published
2025
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50. Context-Dependent and Disease-Specific Diversity in Protein Interactions within Stress Granules

Author

Markmiller, Sebastian, Soltanieh, Sahar, Server, Kari L, Mak, Raymond, Jin, Wenhao, Fang, Mark Y, Luo, En-Ching, Krach, Florian, Yang, Dejun, Sen, Anindya, Fulzele, Amit, Wozniak, Jacob M, Gonzalez, David J, Kankel, Mark W, Gao, Fen-Biao, Bennett, Eric J, Lécuyer, Eric, and Yeo, Gene W

Subjects
Biochemistry and Cell Biology, Biological Sciences, Neurodegenerative, Brain Disorders, ALS, Neurosciences, Aging, Rare Diseases, 2.1 Biological and endogenous factors, Neurological, Amyotrophic Lateral Sclerosis, Animals, Cytoplasmic Granules, Drosophila melanogaster, HEK293 Cells, HeLa Cells, Humans, Neurons, Protein Interaction Maps, Protein Transport, Ribonucleoproteins, Stress, Physiological, Hela Cells, RNA-binding proteins, amyotrophic lateral sclerosis, granules, heat shock, motor neuron disease, neurodegeneration, phase separation, ribonucleoprotein, stress, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Stress granules (SGs) are transient ribonucleoprotein (RNP) aggregates that form during cellular stress and are increasingly implicated in human neurodegeneration. To study the proteome and compositional diversity of SGs in different cell types and in the context of neurodegeneration-linked mutations, we used ascorbate peroxidase (APEX) proximity labeling, mass spectrometry, and immunofluorescence to identify ∼150 previously unknown human SG components. A highly integrated, pre-existing SG protein interaction network in unstressed cells facilitates rapid coalescence into larger SGs. Approximately 20% of SG diversity is stress or cell-type dependent, with neuronal SGs displaying a particularly complex repertoire of proteins enriched in chaperones and autophagy factors. Strengthening the link between SGs and neurodegeneration, we demonstrate aberrant dynamics, composition, and subcellular distribution of SGs in cells from amyotrophic lateral sclerosis (ALS) patients. Using three Drosophila ALS/FTD models, we identify SG-associated modifiers of neurotoxicity in vivo. Altogether, our results highlight SG proteins as central to understanding and ultimately targeting neurodegeneration.

Published
2018

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