Article
Oncology
Fengxiang Li, Yankang Li, Xue Wang, Yingjie Zhang, Xijun Liu, Shanshan Liu, Wei Wang, Jinzhi Wang, Yanluan Guo, Min Xu, Jianbin Li
Summary: This study aimed to investigate the variability in delineating primary esophageal carcinomas (ECs) using different combinations of diagnostic multimodal images. The results showed that using diagnostic multimodal images from endoscopy/EUS, esophagography, and FDG-PET/CT could reduce the intra-/inter-observer variability and increase the accuracy of target delineation for radiation oncologists with advanced medical imaging training and clinical experience.
FRONTIERS IN ONCOLOGY
(2022)
Article
Oncology
Ewan Richard Chapman, Luke Nicholls, Yae-Eun Suh, Vincent Khoo, Daniel Levine, Derfel Ap Dafydd, Nicholas Van As
Summary: This study aims to investigate the inter-observer variability in the contouring of non-spinal bone metastases of prostate cancer using different imaging modalities. The results showed that the use of MRI and PET-CT fusion in CT planning resulted in a significantly larger tumor volume compared to other imaging modalities. The highest consistency in contouring was observed with CT + PET-CT and CT + MRI + PET-CT fusion.
RADIOTHERAPY AND ONCOLOGY
(2023)
Article
Oncology
Diana Veiga-Canuto, Leonor Cerda-Alberich, Cinta Sanguesa Nebot, Blanca Martinez de Las Heras, Ulrike Potschger, Michela Gabelloni, Jose Miguel Carot Sierra, Sabine Taschner-Mandl, Vanessa Duster, Adela Canete, Ruth Ladenstein, Emanuele Neri, Luis Marti-Bonmati
Summary: Tumor segmentation is a crucial step in imaging processing and is usually done manually by radiologists. This study developed an automatic model using the deep learning architecture nnU-Net to detect and segment neuroblastic tumors, and compared its performance with manual segmentation. The results showed that the variability between nnU-Net and manual segmentation is similar to the inter-observer variability in manual segmentation. Additionally, the automatic model demonstrated a significant time advantage over manual segmentation.
Article
Radiology, Nuclear Medicine & Medical Imaging
Tugba Akinci D'Antonoli, Armando Ugo Cavallo, Federica Vernuccio, Arnaldo Stanzione, Michail E. Klontzas, Roberto Cannella, Lorenzo Ugga, Agah Baran, Salvatore Claudio Fanni, Ekaterina Petrash, Ilaria Ambrosini, Luca Alessandro Cappellini, Peter van Ooijen, Elmar Kotter, Daniel Pinto dos Santos, Renato Cuocolo
Summary: This study investigated the reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items' score in a large multireader study. The results showed low inter-rater reliability for total RQS and moderate to good intra-rater reliability for individual RQS items' score. There is a need for a robust and reproducible assessment method to improve the quality of radiomics research.
EUROPEAN RADIOLOGY
(2023)
Article
Oncology
Ebbe Laugaard Lorenzen, Jesper Folsted Kallehauge, Camilla Skinnerup Byskov, Rikke Hedegaard Dahlrot, Charlotte Aaquist Haslund, Trine Lignell Guldberg, Yasmin Lassen-Ramshad, Slavka Lukacova, Aida Muhic, Petra Witt Nystrom, Lene Haldbo-Classen, Ihsan Bahij, Lone Larsen, Britta Weber, Christian Ronn Hansen
Summary: The Danish Neuro Oncology Group (DNOG) has established national consensus guidelines for the delineation of organs at risk (OAR) structures based on published literature. This study found good agreement and low variance in the contours of OARs in the brain, with most structures displaying a median surface distance below 1 mm. The data set generated from this study is being prepared for validation of auto-segmentation algorithms within the Danish Comprehensive Cancer Centre - Radiotherapy and potential collaborators.
Article
Oncology
Florian Kriwanek, Leo Ulbrich, Wolfgang Lechner, Carola Luetgendorf-Caucig, Stefan Konrad, Cora Waldstein, Harald Herrmann, Dietmar Georg, Joachim Widder, Tatjana Traub-Weidinger, Ivo Rausch
Summary: Differences in tumor segmentations between radiation oncologists can cause uncertainty in radiation therapy planning. This study investigated the use of additional somatostatin receptor PET imaging to reduce this variability in the delineation of meningioma. The study also assessed the usability of a threshold-based approach for lesion delineation. The results showed that adding PET information significantly reduced inter observer variability, but the threshold-based approach had only moderate agreement with radiation oncologists.
Article
Obstetrics & Gynecology
Jessica Eastick, Christos Venetis, Simon Cooke, Michael Chapman
Summary: This study aimed to examine the agreement in assessing cytoplasmic string (CS) by embryologists on day 5/6 human blastocysts using EmbryoViewer software. Inter- and intra-observer agreement was calculated, revealing moderate agreement in the presence of CS and their vesicles among the embryologists. The study suggests the need for further training and a quality assurance program to improve agreement in the specific characteristics of CS assessment.
REPRODUCTIVE SCIENCES
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Martin J. Willemink, Domenico Mastrodicasa, Mohammad H. Madani, Marina Codari, Leonid L. Chepelev, Gabriel Mistelbauer, Kate Hanneman, Maral Ouzounian, Daniel Ocazionez, Rana O. Afifi, Joan M. Lacomis, Luigi Lovato, Davide Pacini, Gianluca Folesani, Ricarda Hinzpeter, Hatem Alkadhi, Arthur E. Stillman, Anna M. Sailer, Valery L. Turner, Virginia Hinostroza, Kathrin Baumler, Anne S. Chin, Nicholas S. Burris, D. Craig Miller, Michael P. Fischbein, Dominik Fleischmann
Summary: This study aimed to evaluate the reproducibility of expert-derived CTA measurements. The results showed that by optimizing the manual measurement method, inter-observer consistency can be improved, which is crucial for the ground-truth determination of machine learning models.
EUROPEAN RADIOLOGY
(2023)
Article
Multidisciplinary Sciences
Weijun Chen, Cheng Wang, Wenming Zhan, Yongshi Jia, Fangfang Ruan, Lingyun Qiu, Shuangyan Yang, Yucheng Li
Summary: Deep learning auto-segmentation technology provides good auto-contouring results for most organs in the chest and abdomen, meeting clinical planning requirements with slight modifications. However, using Atlas for auto-contouring yields inferior results compared to deep learning auto-segmentations, with only some organs usable clinically after modifications.
SCIENTIFIC REPORTS
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Roxana Fu, Joseph K. Leader, Tejus Pradeep, Junli Shi, Xin Meng, Yanchun Zhang, Jiantao Pu
Summary: The study developed and validated a deep learning algorithm to automatically detect and segment orbital abscess on head CT scans, showing promising performance in strong agreement with a human observer.
Article
Biology
Daisuke Kawahara, Masato Tsuneda, Shuichi Ozawa, Hiroyuki Okamoto, Mitsuhiro Nakamura, Teiji Nishio, Akito Saito, Yasushi Nagata
Summary: The current study proposes an auto-segmentation model using a stepwise deep neural network on CT images of head and neck cancer. The results show that the stepwise-network outperforms the atlas-based method and conventional U-net, indicating its potential value in improving the efficiency of head and neck radiotherapy treatment planning.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
F. D'Argento, A. Pedicelli, C. Ciardi, E. Leone, M. Scarabello, A. Infante, A. Alexandre, E. Lozupone, I. Valente, C. Colosimo
Summary: The study compared the size and morphologic features of three-dimensional aneurysm models obtained using different methods, finding some differences in dimensions and characteristics. Differences were observed between manual and automatic measurements of aneurysm dimensions. Additionally, the presence of subarachnoid hemorrhage did not affect the three-dimensional reconstruction of aneurysms.
Article
Oncology
Shihong Nie, Yuanfeng Wei, Fen Zhao, Ya Dong, Yan Chen, Qiaoqi Li, Wei Du, Xin Li, Xi Yang, Zhiping Li
Summary: This study developed an AI-assisted system for automatic contouring of the clinical target volume (CTV) and organs-at-risk (OARs) in cervical cancer radiotherapy. The results showed that the proposed SegNet, trained with multi-group data, achieved better accuracy and quality in automatic CTV contouring compared to other methods. The use of the AI-assisted system can shorten the time required for manual contouring without compromising quality.
RADIATION ONCOLOGY
(2022)
Article
Engineering, Biomedical
Xianjin Dai, Yang Lei, Tonghe Wang, Anees H. Dhabaan, Mark McDonald, Jonathan J. Beitler, Walter J. Curran, Jun Zhou, Tian Liu, Xiaofeng Yang
Summary: The study aims to develop a fully automated approach for rapid and accurate multi-organ contouring in head-and-neck cancer patients using synthetic MRI and CBCT technology. By combining the information provided by MRI and CBCT, accurate multi-organ segmentation in HN cancer patients is expected. The proposed method shows promising results in terms of DSC values and can be a valuable tool for adaptive radiation therapy.
PHYSICS IN MEDICINE AND BIOLOGY
(2021)
Article
Oncology
Guillaume Vogin, Liza Hettal, Clarisse Bartau, Juliette Thariat, Marie-Virginie Claeys, Guillaume Peyraga, Paul Retif, Ulrike Schick, Delphine Antoni, Zsuzsa Bodgal, Frederic Dhermain, Loic Feuvret
Summary: The study evaluated inter-individual variability in delineation of common cranial organs at risk in neurooncology practice. Results showed higher Kappa index for larger OAR and lower for smaller OAR. Radiation oncologists performed better in all indicators compared to non-members.
RADIATION ONCOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Petros Kalendralis, Matthijs Sloep, Ananya Choudhury, Lerau Seyben, Jasper Snel, Nibin Moni George, Joeri Veugen, Martijn Veening, Johannes A. Langendijk, Andre Dekker, Johan van Soest, Rianne Fijten
Summary: This study provides an overview of the tumour group data lists in the Dutch national proton therapy registry, and presents a knowledge graph approach using ontologies and semantic web technologies to describe head and neck tumour variables. The goal is to offer a flexible and interoperable data model for data exchange in the radiotherapy community, highlighting variables needed for the model-based approach.
Article
Chemistry, Analytical
Suraj Pai, Ibrahim Hadzic, Chinmay Rao, Ivan Zhovannik, Andre Dekker, Alberto Traverso, Stylianos Asteriadis, Enrique Hortal
Summary: Research on CycleGAN-based synthetic image generation in the medical community has increased due to its effective utilization of unpaired images. However, the presence of artifacts in the generated images limits its reliability for medical imaging purposes. To overcome this, a generalized frequency-based loss is proposed to preserve frequency content. The proposed methods outperform the baseline CycleGAN and existing techniques, exhibiting quantitatively and qualitatively improved results without introducing artifacts or loss in image quality. Furthermore, the generated sCTs demonstrate superior performance compared to original CBCT images in downstream tasks.
Article
Oncology
John S. Peterson, Deborah Plana, Danielle S. Bitterman, Skyler Bryce Johnson, Hugo J. W. L. Aerts, Benjamin Harris Kann
Summary: The study found that the content of eligibility criteria in NCI-affiliated trials is growing rapidly and is strongly associated with accrual failure. These findings support initiatives to simplify eligibility criteria and suggest that further efforts are needed to improve cancer trial accrual.
Article
Oncology
Audrey R. Freischel, Jamie K. Teer, Kimberly Luddy, Jessica Cunningham, Yael Artzy-Randrup, Tamir Epstein, Kenneth Y. Tsai, Anders Berglund, John L. Cleveland, Robert J. Gillies, Joel S. Brown, Robert A. Gatenby
Summary: Evolution plays a crucial role in the initiation and progression of cancer. In addition to driver mutations, natural selection conserves genes that are necessary for optimal cancer cell fitness. By studying subtypes of lung adenocarcinoma, we identified highly mutated and highly conserved genes, which have common utility in adapting to similar tissue environments and are critical for optimal fitness. Targeting tumor-specific conserved genes may represent an effective treatment strategy.
Article
Computer Science, Interdisciplinary Applications
Felix Busch, Lina Xu, Dmitry Sushko, Matthias Weidlich, Daniel Truhn, Gustav Mueller-Franzes, Maurice M. Heimer, Stefan M. Niehues, Marcus R. Makowski, Markus Hinsche, Janis L. Vahldiek, Hugo J. W. L. Aerts, Lisa C. Adams, Keno K. Bressem
Summary: This study aims to develop an efficient convolutional neural network for automatic anatomy segmentation of bedside chest radiographs (CXRs). By using a human-in-the-loop segmentation workflow with an active learning approach, the performance of artificial intelligence in diagnosing cardiothoracic disease and invasive therapy devices is improved. The final model achieved comparable performance to state-of-the-art approaches.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Oncology
Fariba Tohidinezhad, Dennis Bontempi, Zhen Zhang, Anne-Marie Dingemans, Joachim Aerts, Gerben Bootsma, Johan Vansteenkiste, Sayed Hashemi, Egbert Smit, Hester Gietema, Hugo J. W. L. Aerts, Andre Dekker, Lizza E. L. Hendriks, Alberto Traverso, Dirk De Ruysscher
Summary: This study aimed to develop a prediction model to differentiate immunotherapy-induced pneumonitis (IIP) from other types of pneumonitis (OTP) in non-small cell lung cancer patients. The radiomic biomarkers applied to computed tomography imaging can assist clinicians in making a diagnosis when the radiological assessment is inconclusive.
EUROPEAN JOURNAL OF CANCER
(2023)
Article
Health Care Sciences & Services
Lisa C. Adams, Felix Busch, Daniel Truhn, Marcus R. Makowski, Hugo J. W. L. Aerts, Keno K. Bressem
Summary: DALL-E 2 shows promising capabilities in generating and manipulating x-ray images, but has limitations in generating images with pathological abnormalities or other medical imaging modalities.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Multidisciplinary Sciences
Kevin Ng, Jesse Boumelha, Katey S. S. Enfield, Jorge L. Almagro, Hongui M. Cha, Oriol Pich, Takahiro Karasaki, David Moore, Roberto Salgado, Monica Sivakumar, George Young, Miriam L. Molina-Arcas, Sophie de Carne Trecesson, Panayiotis Anastasiou, Annika C. Fendler, Lewis Au, Scott T. C. Shepherd, Carlos Martinez-Ruiz, Clare Puttick, James R. M. Black, Thomas B. K. Watkins, Hyemin Kim, Seohee Shim, Nikhil Faulkner, Jan A. Attig, Selvaraju Veeriah, Neil J. Magno, Sophia T. Ward, Alexander Frankell, Maise Al Bakir, Emilia Lim, Mark Hill, Gareth Wilson, Daniel Cook, Nicolai Birkbak, Axel Behrens, Nadia Yousaf, Sanjay Popat, Allan Hackshaw, TRACERx Consortium, CAPTURE Consortium, Crispin T. Hiley, Kevin Litchfield, Nicholas McGranahan, Mariam Jamal-Hanjani, James Larkin, Se-Hoon Lee, Samra Turajlic, Charles Swanton, Julian Downward, George Kassiotis
Summary: This study reveals that lung adenocarcinomas in both humans and mice elicit local germinal center responses and tumour-binding antibodies, with endogenous retrovirus (ERV) envelope glycoproteins as the dominant anti-tumour antibody target. ERV-targeting B cell responses are enhanced by immune checkpoint blockade (ICB) and targeted inhibition of KRAS(G12C). ERV-reactive antibodies have anti-tumour activity and improve survival in a mouse model, and ERV expression predicts the response to ICB in human lung adenocarcinoma. Furthermore, the study demonstrates that effective immunotherapy in the mouse model requires CXCL13-dependent tertiary lymphoid structure (TLS) formation, and therapeutic CXCL13 treatment enhances anti-tumour immunity and synergizes with ICB. These findings provide a potential mechanistic basis for the association between TLS and immunotherapy response.
Article
Multidisciplinary Sciences
Maise Al Bakir, Ariana Huebner, Carlos Martinez-Ruiz, Kristiana Grigoriadis, Thomas B. K. Watkins, Oriol Pich, David A. Moore, Selvaraju Veeriah, Sophia Ward, Joanne Laycock, Diana Johnson, Andrew Rowan, Maryam Razaq, Mita Akther, Cristina Naceur-Lombardelli, Paulina Prymas, Antonia Toncheva, Sonya Hessey, Michelle Dietzen, Emma Colliver, Alexander Frankell, Abigail Bunkum, Emilia L. Lim, Takahiro Karasaki, Christopher Abbosh, Crispin T. Hiley, Mark S. Hill, Daniel E. Cook, Gareth A. Wilson, Roberto Salgado, Emma Nye, Richard Kevin Stone, Dean A. Fennell, Gillian Price, Keith M. Kerr, Babu Naidu, Gary Middleton, Yvonne Summers, Colin R. Lindsay, Fiona H. Blackhall, Judith Cave, Kevin G. Blyth, Arjun Nair, Asia Ahmed, Magali N. Taylor, Alexander James Procter, Mary Falzon, David Lawrence, Neal Navani, Ricky M. Thakrar, Sam M. Janes, Dionysis Papadatos-Pastos, Martin D. Forster, Siow Ming Lee, Tanya Ahmad, Sergio Quezada, Karl S. Peggs, Peter Van Loo, Caroline Dive, Allan Hackshaw, Nicolai J. Birkbak, Simone Zaccaria, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton, Jason F. Lester, Amrita Bajaj, Apostolos Nakas, Azmina Sodha-Ramdeen, Keng Ang, Mohamad Tufail, Mohammed Fiyaz Chowdhry, Molly Scotland, Rebecca Boyles, Sridhar Rathinam, Claire Wilson, Domenic Marrone, Sean Dulloo, Gurdeep Matharu, Jacqui A. Shaw, Joan Riley, Lindsay Primrose, Ekaterini Boleti, Heather Cheyne, Mohammed Khalil, Shirley Richardson, Tracey Cruickshank, Sarah Benafif, Kayleigh Gilbert, Akshay J. Patel, Aya Osman, Christer Lacson, Gerald Langman, Helen Shackleford, Madava Djearaman, Salma Kadiri, Angela Leek, Jack Davies Hodgkinson, Nicola Totten, Angeles Montero, Elaine Smith, Eustace Fontaine, Felice Granato, Helen Doran, Juliette Novasio, Kendadai Rammohan, Leena Joseph, Paul Bishop, Rajesh Shah, Stuart Moss, Vijay Joshi, Philip Crosbie, Fabio Gomes, Kate Brown, Mathew Carter, Anshuman Chaturvedi, Lynsey Priest, Pedro Oliveira, Matthew G. Krebs, Alexandra Clipson, Jonathan Tugwood, Alastair Kerr, Dominic G. Rothwell, Elaine Kilgour, Hugo J. W. L. Aerts, Roland F. Schwarz, Tom L. Kaufmann, Rachel Rosenthal, Zoltan Szallasi, Judit Kisistok, Mateo Sokac, Miklos Diossy, Jonas Demeulemeester, Aengus Stewart, Alastair Magness, Angeliki Karamani, Benny Chain, Brittany B. Campbell, Carla Castignani, Chris Bailey, Clare Puttick, Clare E. Weeden, Claudia Lee, Corentin Richard, David R. Pearce, Despoina Karagianni, Dhruva Biswas, Dina Levi, Elena Hoxha, Elizabeth Larose Cadieux, Eva Gronroos, Felip Galvez-Cancino, Foteini Athanasopoulou, Francisco Gimeno-Valiente, George Kassiotis, Georgia Stavrou, Gerasimos Mastrokalos, Haoran Zhai, Helen L. Lowe, Ignacio Matos, Jacki Goldman, James L. Reading, James R. M. Black, Javier Herrero, Jayant K. Rane, Jerome Nicod, Jie Min Lam, John A. Hartley, Katey S. S. Enfield, Kayalvizhi Selvaraju, Kerstin Thol, Kevin Litchfield, Kevin W. Ng, Kezhong Chen, Krijn Dijkstra, Krupa Thakkar, Leah Ensell, Mansi Shah, Marcos Vasquez, Maria Litovchenko, Mariana Werner Sunderland, Michelle Leung, Mickael Escudero, Mihaela Angelova, Miljana Tanic, Monica Sivakumar, Nnennaya Kanu, Olga Chervova, Olivia Lucas, Othman Al-Sawaf, Philip Hobson, Piotr Pawlik, Robert Bentham, Robert E. Hynds, Roberto Vendramin, Sadegh Saghafinia, Saioa Lopez, Samuel Gamble, Seng Kuong Anakin Ung, Sharon Vanloo, Stefan Boeing, Stephan Beck, Supreet Kaur Bola, Tamara Denner, Teresa Marafioti, Thanos P. Mourikis, Victoria Spanswick, Vittorio Barbe, Wei-Ting Lu, William Hill, Wing Kin Liu, Yin Wu, Yutaka Naito, Zoe Ramsden, Catarina Veiga, Gary Royle, Charles-Antoine Collins-Fekete, Francesco Fraioli, Paul Ashford, Tristan Clark, Elaine Borg, James Wilson, Davide Patrini, Emilie Martinoni Hoogenboom, Fleur Monk, James W. Holding, Junaid Choudhary, Kunal Bhakhri, Marco Scarci, Martin Hayward, Nikolaos Panagiotopoulos, Pat Gorman, Reena Khiroya, Robert C. M. Stephens, Yien Ning Sophia Wong, Steve Bandula, Abigail Sharp, Sean Smith, Nicole Gower, Harjot Kaur Dhanda, Kitty Chan, Camilla Pilotti, Rachel Leslie, Anca Grapa, Hanyun Zhang, Khalid AbdulJabbar, Xiaoxi Pan, Yinyin Yuan, David Chuter, Mairead MacKenzie, Serena Chee, Aiman Alzetani, Lydia Scarlett, Jennifer Richards, Papawadee Ingram, Silvia Austin, Eric Lim, Paulo De Sousa, Simon Jordan, Alexandra Rice, Hilgardt Raubenheimer, Harshil Bhayani, Lyn Ambrose, Anand Devaraj, Hema Chavan, Sofina Begum, Silviu Buderi, Daniel Kaniu, Mpho Malima, Sarah Booth, Andrew G. Nicholson, Nadia Fernandes, Pratibha Shah, Chiara Proli, Madeleine Hewish, Sarah Danson, Michael J. Shackcloth, Lily Robinson, Peter Russell, Craig Dick, John Le Quesne, Alan Kirk, Mo Asif, Rocco Bilancia, Nikos Kostoulas, Mathew Thomas
Summary: Through longitudinal evolutionary analysis of 126 non-small cell lung cancer tumors, it was found that 25% of cases showed early metastatic divergence before the last clonal sweep in the primary tumor, which was enriched in patients who were smokers at the time of initial diagnosis. Polyclonal dissemination was associated with tumor recurrence, while primary lymph node disease played a limited role in metastatic relapse. Our study highlights the importance of selection in metastatic clone evolution within untreated primary tumors.
Article
Oncology
Zhen Zhang, Zhixiang Wang, Tianchen Luo, Meng Yan, Andre Dekker, Dirk De Ruysscher, Alberto Traverso, Leonard Wee, Lujun Zhao
Summary: The purpose of this study is to develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in lung cancer patients who received radical (chemo)radiotherapy. CT, RD images, and clinical parameters were obtained from a training set of 314 retrospectively-collected patients and a test set of 35 prospectively-collected patients. External validation was conducted using patients from a clinical trial. The results showed that the deep learning approach effectively and accurately predicted the occurrence of RP.
RADIOTHERAPY AND ONCOLOGY
(2023)
Article
Oncology
Maria Antonietta Gambacorta, Giuditta Chiloiro, Carlotta Masciocchi, Silvia Mariani, Angela Romano, Alessandra Gonnelli, Jean-Pierre Gerard, Samuel Ngan, Claus Roedel, Krzysztof Bujko, Robert Glynne-Jones, Johan van Soest, Andre Dekker, Andrea Damiani, Vincenzo Valentini
Summary: This study conducted a pooled analysis on a large cohort of rectal cancer patients to assess the role of pCR and 2yDFS as surrogate endpoints for OS. The results contribute to understanding the prognostic role of these two endpoints and can help identify high-risk patients for personalized treatments.
Article
Medical Informatics
Benjamin H. Kann, Jirapat Likitlersuang, Dennis Bontempi, Zezhong Ye, Sanjay Aneja, Richard Bakst, Hillary R. Kelly, Amy F. Juliano, Sam Payabvash, Jeffrey P. Guenette, Ravindra Uppaluri, Danielle N. Margalit, Jonathan D. Schoenfeld, Roy B. Tishler, Robert Haddad, Hugo J. W. L. Aerts, Joaquin J. Garcia, Yael Flamand, Rathan M. Subramaniam, Barbara A. Burtness, Robert L. Ferris
Summary: This study evaluated a CT-based deep learning algorithm for predicting pathological extranodal extension (ENE) in patients with HPV-associated oropharyngeal carcinoma. The algorithm showed better performance than four board-certified head and neck radiologists in classifying ENE, suggesting its potential as a treatment selection tool.
LANCET DIGITAL HEALTH
(2023)
Article
Food Science & Technology
Anand Gavai, Yamine Bouzembrak, Wenjuan Mu, Frank Martin, Rajaram Kaliyaperumal, Johan van Soest, Ananya Choudhury, Jaap Heringa, Andre Dekker, Hans J. P. Marvin
Summary: Artificial Intelligence (AI) based algorithms, particularly data driven Bayesian Network (BN) models, are suitable for predicting future food fraud and enabling timely actions by food producers. However, data sharing is hindered due to various concerns, such as interests, security, and privacy. Federated learning (FL) technology can address these issues, allowing integration of data from different sources while ensuring data privacy and confidentiality. This research demonstrates the potential of FL for food fraud control and decision-making in the food supply chain.
NPJ SCIENCE OF FOOD
(2023)
Article
Microbiology
Yanchao Zhang, Aleksandra M. Kubiak, Tom S. Bailey, Luuk Claessen, Philip Hittmeyer, Ludwig Dubois, Jan Theys, Philippe Lambin
Summary: Clostridium species have gained attention in industrial and medical applications, with the development of genetic tools enabling the advancement of the CRISPR-Cas systems. This study demonstrated the establishment of a CRISPR-Cas12a system in clostridia with two different cas12a genes, allowing for efficient and rapid genome modification. The results showed that the CRISPR-FnCas12a system offers flexible target selection in clostridia, with a specific folding pattern of the precursor crRNA being important for high mutation generation efficiency.
MICROBIOLOGY SPECTRUM
(2023)
Article
Computer Science, Artificial Intelligence
Keno K. Bressem, Jens-Michalis Papaioannou, Paul Grundmann, Florian Borchert, Lisa C. Adams, Leonhard Liu, Felix Busch, Lina Xu, Jan P. Loyen, Stefan M. Niehues, Moritz Augustin, Lennart Grosser, Marcus R. Makowski, Hugo J. W. L. Aerts, Alexander Loeser
Summary: This paper presents medBERT.de, a pre-trained German BERT model designed specifically for the German medical domain. The model achieves state-of-the-art performance on various medical benchmarks and the analysis investigates the impact of data deduplication and tokenization methods on the model's performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Oncology
Derek A. Mumaw, Allison J. Hazy, Aleksander Vayntraub, Thomas J. Quinn, Kamran Salari, John H. Chang, Noah Kalman, Sanford Katz, James Urbanic, Robert H. Press, Arpi Thukral, Henry Tsai, George E. Laramore, Jason Molitoris, Carlos Vargas, Samir H. Patel, Craig Stevens, Rohan L. Deraniyagala
Summary: This study evaluated contralateral recurrences in patients with oropharyngeal squamous cell carcinoma who received unilateral proton beam therapy. The results showed a favorable contralateral neck failure rate that was comparable to photon irradiation.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Kangpyo Kim, Dongryul Oh, Jae Myoung Noh, Yang Won Min, Hong Kwan Kim, Yong Chan Ahn
Summary: This study suggests that hypofractionated radiation therapy alone is a feasible option for early stage esophageal squamous cell carcinoma patients. Particularly, in patients with tumor length < 3 cm, this treatment scheme shows favorable local control rates with low incidence of esophageal toxicities.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Lin Chen, Jing Li, Kunpeng Li, Jiang Hu, Qingjie Li, Chenglong Huang, Gaoyuan Wang, Na Liu, Linglong Tang
Summary: This study analyzed the probability of hearing impairment after radiotherapy for nasopharyngeal carcinoma and developed a predictive model, providing dose limitation suggestions to improve patients' quality of life.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Yiwei Yang, Jianxin Wang, Feng Gao, Zhen Liu, Tangzhi Dai, Haowen Zhang, Hongyu Zhu, Tingting Wang, Dexin Xiao, Kui Zhou, Zheng Zhou, Dai Wu, Xiaobo Du, Sen Bai
Summary: This paper provides a comprehensive description of the current status of PARTER, which is the first experimental FLASH platform utilizing megavoltage X-rays. It showcases the reliable performance and stability of the dosimeters and monitors used in PARTER, as well as the satisfactory dose distribution and characteristics of the FLASH X-rays. The platform effectively meets the requirements of preclinical research on megavoltage X-ray FLASH and undergoes continuous upgrades.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Maria Thor, Kelly Fitzgerald, Aditya Apte, Jung Hun Oh, Aditi Iyer, Otasowie Odiase, Saad Nadeem, Ellen D. Yorke, Jamie Chaft, Abraham J. Wu, Michael Offin, Charles B. Simone Ii, Isabel Preeshagul, Daphna Y. Gelblum, Daniel Gomez, Joseph O. Deasy, Andreas Rimner
Summary: The purpose of this study was to identify predictors of disease progression in early-stage non-small cell lung cancer (NSCLC) patients after receiving definitive stereotactic body radiation therapy (SBRT). The results showed that tumor diameter and SUVmax were the most frequently reported features associated with progression/survival, and a re-fitted model including these two features had the best performance.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Yong-Qiao He, Tong-Min Wang, Da-Wei Yang, Wen-Qiong Xue, Chang-Mi Deng, Dan-Hua Li, Wen-Li Zhang, Ying Liao, Ruo-Wen Xiao, Lu-Ting Luo, Hua Diao, Xia-Ting Tong, Yan-Xia Wu, Xue-Yin Chen, Jiang-Bo Zhang, Ting Zhou, Xi-Zhao Li, Pei-Fen Zhang, Xiao-Hui Zheng, Shao-Dan Zhang, Ye-Zhu Hu, Guan-Qun Zhou, Jun Ma, Ying Sun, Wei-Hua Jia
Summary: In this study, researchers aimed to establish a predictive model for radiation-induced brain injury (RBI) in nasopharyngeal carcinoma (NPC) patients by incorporating clinical factors and newly developed genetic variants. They conducted a large-scale retrospective study and a genome-wide association study to develop a polygenic risk score (PRS) for RBI risk prediction. The results showed that the PRS, combined with clinical factors, improved the accuracy of RBI risk stratification and suggested personalized radiotherapy.
RADIOTHERAPY AND ONCOLOGY
(2024)
Review
Oncology
Xiaoyong Xiang, Zhe Ji, Jing Jin
Summary: A review of studies suggests that brachytherapy as a salvage therapy for recurrent glioblastoma shows acceptable safety and good post-treatment clinical efficacy for selected patients.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
M. Berbee, C. T. Muijs, F. E. M. Voncken, L. Wee, M. Sosef, B. van Etten, J. W. van Sandick, F. A. R. M. Warmerdam, J. J. de Haan, E. Oldehinkel, J. M. van Dieren, L. Boersma, J. A. Langendijk, A. van der Schaaf, J. B. Reitsma, E. Schuit
Summary: This study externally validated a model for predicting 2-year total mortality in lung cancer patients in esophageal cancer patients. The intercept and/or slope of the original model needed adjustment to achieve good performance in esophageal cancer patients.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Dominique Reijtenbagh, Jeremy Godart, Joan Penninkhof, Sandra Quint, Andras Zolnay, Jan-Willem Mens, Mischa Hoogeman
Summary: This study compared the performance of the current PotD strategy with non-adaptive and fully online-adaptive techniques in the treatment of cervical cancer patients. The findings show that the PotD protocol is effective in improving normal tissue sparing compared to no adaptation, while fully online-adaptive approaches can further reduce target volume but come with a more complex workflow.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Albrecht Weiss, Steffen Loeck, Ting Xu, Zhongxing Liao, Aswin L. Hoffmann, Esther G. C. Troost
Summary: Traditional models for predicting radiation pneumonitis may not be applicable to non-small cell lung cancer patients treated with passively-scattered proton therapy. The use of effective alpha/beta parameter can predict the occurrence of radiation pneumonitis in these patients.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Z. A. R. Gouw, J. Jeong, A. Rimner, N. Y. Lee, A. Jackson, A. Fu, J-j. Sonke, J. O. Deasy
Summary: This study investigates the effectiveness of non-uniform fractionation schedules in radiotherapy for early-stage non-small cell lung cancer. Through modeling, optimized schedules are proposed to minimize local failures and toxicity risk. The results suggest that non-standard primer shot fractionation can reduce hypoxia-induced radioresistance and improve treatment outcomes.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Sara Ronchi, Alessandro Cicchetti, Maria Bonora, Rossana Ingargiola, Anna Maria Camarda, Stefania Russo, Sara Imparato, Paolo Castelnuovo, Ernesto Pasquini, Piero Nicolai, Mohssen Ansarin, Michele Del Vecchio, Marco Benazzo, Ester Orlandi, Barbara Vischioni
Summary: This study evaluates the efficacy and toxicity of carbon ion radiotherapy (CIRT) in locally advanced head and neck mucosal melanoma patients. The results show that CIRT is safe and effective in treating the local region, and immunotherapy after relapse can improve overall survival. However, further prospective trials are needed to assess the role of targeted/immune- systemic therapy in this disease.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Dominik Wawrzuta, Justyna Klejdysz, Marzanna Chojnacka
Summary: This study analyzed articles about radiation oncology published in The New York Times since its inception in 1851, and identified changes in media sentiment and prevalent themes related to radiotherapy. The findings suggest an increasing negative sentiment in media coverage towards radiotherapy, with a shift towards reporting treatment errors, toxicity, and ineffectiveness.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Elaine Limkin, Pierre Blanchard, Benjamin Lacas, Jean Bourhis, Mahesh Parmar, Lisa Licitra, Quynh-Thu Le, Sue S. Yom, Catherine Fortpied, Johannes Langendijk, Jan B. Vermorken, Jacques Bernier, Jens Overgaard, Jonathan Harris, Jean-Pierre Pignon, Anne Auperin
Summary: This study investigated the impact of season of radiotherapy on the outcomes of head and neck squamous cell cancer patients. The results showed that the season of radiotherapy did not have any significant effect on patient outcomes.
RADIOTHERAPY AND ONCOLOGY
(2024)
Article
Oncology
Fabio L. Cury, Gustavo A. Viani, Andre G. Gouveia, Camila V. S. Freire, Gabriel de A. Grisi, Fabio Y. Moraes
Summary: In limb-sparing treatment of soft tissue sarcoma patients, a 5-day course of preoperative radiotherapy results in high local control and favorable R0 margins, with acceptable complication rates, particularly for patients receiving higher biological equivalent doses.
RADIOTHERAPY AND ONCOLOGY
(2024)
