Article
Multidisciplinary Sciences
Ou Yamaguchi, Kyoichi Kaira, Ichiro Naruse, Yukihiro Umeda, Takeshi Honda, Satoshi Watanabe, Kosuke Ichikawa, Kazunari Tateishi, Norimitsu Kasahara, Tetsuya Higuchi, Kosuke Hashimoto, Shun Shinomiya, Yu Miura, Ayako Shiono, Atsuto Mouri, Hisao Imai, Kunihiko Iizuka, Tamotsu Ishizuka, Koichi Minato, Satoshi Suda, Hiroshi Kagamu, Keita Mori, Ichiei Kuji, Nobuhiko Seki
Summary: This study aimed to investigate the clinical relevance of F-18-FDG PET/CT compared to CT in predicting the response to PD-1 blockade in the early phase. The results showed that metabolic assessment by MTV or TLG was superior to morphological changes on CT for predicting the therapeutic response and survival.
SCIENTIFIC REPORTS
(2022)
Article
Oncology
Ming-li Ouyang, Yi-ran Wang, Qing-shan Deng, Ye-fei Zhu, Zhen-hua Zhao, Ling Wang, Liang-xing Wang, Kun Tang
Summary: This study developed a PET radiomic model in combination with CT imaging features to differentiate LN metastasis in NSCLC patients, demonstrating good diagnostic efficacy.
FRONTIERS IN ONCOLOGY
(2021)
Article
Multidisciplinary Sciences
Makito Suga, Ryuichi Nishii, Kenta Miwa, Yuto Kamitaka, Kana Yamazaki, Kentaro Tamura, Naoyoshi Yamamoto, Ryosuke Kohno, Masato Kobayashi, Katsuyuki Tanimoto, Hiroshi Tsuji, Tatsuya Higashi
Summary: The study aimed to evaluate the ability of F-18-FDG PET/CT metabolic parameters and its textural image features to differentiate NSCLC from RP, and confirmed that texture parameters can improve diagnostic accuracy.
SCIENTIFIC REPORTS
(2021)
Article
Multidisciplinary Sciences
Pauline Bourigault, Michael Skwarski, Ruth E. Macpherson, Geoff S. Higgins, Daniel R. McGowan
Summary: This study investigated the correlation between 2-hour and 4-hour post-injection FMISO-PET imaging in NSCLC patients and found that imaging at 4 hours after injection provided better results. There was a strong correlation between the results of the 2-hour and 4-hour scans in overall tumors, but the correlation decreased from the center to the edge in tumor subregions.
SCIENTIFIC REPORTS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Mourtaza Karimdjee, Gauthier Delaby, Damien Huglo, Clio Baillet, Alexandre Willaume, Simon Dujardin, Alban Bailliez
Summary: New PET data-processing tools using artificial intelligence (AI) provide automated lesion selection and segmentation, allowing for the routine acquisition of total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) at the clinical workstation. This study evaluated the implementation of AI in commercial software, demonstrating its reproducibility, time savings, and reliable performance. The results showed that AI-enabled software offers an automated, fast, and consistently performing tool for obtaining TMTV and TLG.
EUROPEAN RADIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Wan-Ming Chen, Mingchih Chen, Jeng-Guan Hsu, Tian-Shyug Lee, Ben-Chang Shia, Szu-Yuan Wu
Summary: The use of preoperative PET/CT is associated with a lower risk of death in patients with resectable stage IIIA-IIIB non-small cell lung cancer, but it does not improve survival rates in patients with stage I-II cancer.
Article
Immunology
Haipeng Tong, Jinju Sun, Jingqin Fang, Mi Zhang, Huan Liu, Renxiang Xia, Weicheng Zhou, Kaijun Liu, Xiao Chen
Summary: The study established a machine learning model to predict tumor immune status in non-small cell lung cancer (NSCLC) using F-18-FDG PET/CT radiomics and clinical characteristics. The PET/CT radiomics model outperformed the CT model in predicting CD8 expression, and the combined radiomics-clinical model performed the best.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Veterinary Sciences
Dohee Lee, Taesik Yun, Yoonhoi Koo, Yeon Chae, Dongwoo Chang, Mhan-Pyo Yang, Byeong-Teck Kang, Hakhyun Kim
Summary: This is the first case report of using 18F-FDG PET/CT to diagnose suspected adrenal cortical carcinoma in a dog, providing valuable information for its diagnosis.
BMC VETERINARY RESEARCH
(2022)
Article
Medicine, General & Internal
David Morland, Marco Chiappetta, Pierre-Emmanuel Falcoz, Marie-Pierre Chenard, Salvatore Annunziata, Luca Boldrini, Filippo Lococo, Alessio Imperiale
Summary: The study aimed to construct a PET model to improve lymph node assessment in non-small cell lung carcinoma (NSCLC). A multivariate model combining cN0 status and primary tumor SUVmax was selected and applied to a validation set. The model showed improved prediction of N status with higher specificity compared to visual interpretation alone.
FRONTIERS IN MEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Zhonghang Zheng, Jie Wang, Weiyue Tan, Yi Zhang, Jing Li, Ruiting Song, Ligang Xing, Xiaorong Sun
Summary: The objective of this study was to establish an 18F-FDG PET/CT radiomics model for predicting brain metastasis in NSCLC patients. By investigating metabolic indicators, CT features, and clinical features, independent predictive factors of brain metastasis were identified. A prediction model was established by incorporating radiomics signature and clinicopathological risk variables, which showed good performance in terms of discrimination, calibration, and clinical application.
EUROPEAN JOURNAL OF RADIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Kailin Qiao, Xueting Qin, Shuai Fu, Jiazhong Ren, Jing Jia, Xinying Hu, Yuanyuan Tao, Shuanghu Yuan, Yuchun Wei
Summary: The uptake of [F-18]AlF-NOTA-FAPI-04 and [F-18]FDG imaging tracers varies in malignant and inflammatory lung lesions, with lower uptake observed in inflammatory lesions. This difference in uptake can be valuable for distinguishing between malignancy and various inflammatory findings.
EUROPEAN RADIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hai-Jeon Yoon, Kyoungjune Pak
Summary: This study aimed to assess the impact of F-18-FDG PET on the management of lung cancer, revealing its significant role in detecting lung cancer recurrence/metastasis, especially in cases involving equivocal or suspicious recurrence/metastasis on conventional imaging.
CLINICAL NUCLEAR MEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Lili Wang, Tiancheng Li, Junjie Hong, Mingyue Zhang, Mingli Ouyang, Xiangwu Zheng, Kun Tang
Summary: This study developed a radiomics model based on PET images for predicting lymph node metastasis in solid lung adenocarcinoma patients. The model demonstrated good predictive accuracy and clinical utility in preoperative prediction of occult lymph node metastasis.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yuki Onozato, Takekazu Iwata, Yasufumi Uematsu, Daiki Shimizu, Takayoshi Yamamoto, Yukiko Matsui, Kazuyuki Ogawa, Junpei Kuyama, Yuichi Sakairi, Eiryo Kawakami, Toshihiko Iizasa, Ichiro Yoshino
Summary: This study developed and validated multiple machine learning models using radiomic features from preoperative [F-18]fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) images to predict the pathological invasiveness of lung cancer.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Long Zhao, Jinjun Liu, Huoqiang Wang, Jingyun Shi
Summary: This study found a significant association between SUVmax and PD-L1 expression in NSCLC and ADC, with significant differences in SUVmax between high and low PD-L1 expression conditions as quantified using the PD-L1 22C3 assay. The results suggest that SUVmax may serve as a metabolic biomarker to help select NSCLC patients who are likely to benefit from pembrolizumab.
BRITISH JOURNAL OF RADIOLOGY
(2021)
Review
Chemistry, Medicinal
Julien Guiot, Akshayaa Vaidyanathan, Louis Deprez, Fadila Zerka, Denis Danthine, Anne-Noelle Frix, Philippe Lambin, Fabio Bottari, Nathan Tsoutzidis, Benjamin Miraglio, Sean Walsh, Wim Vos, Roland Hustinx, Marta Ferreira, Pierre Lovinfosse, Ralph T. H. Leijenaar
Summary: Radiomics is a method for quantitatively analyzing medical images to create diagnostic, prognostic, and/or predictive models. It utilizes sophisticated image analysis tools and statistical methods to extract hidden information in medical images, but caution is needed to avoid overenthusiastic claims and scientific pollution.
MEDICINAL RESEARCH REVIEWS
(2022)
Article
Oncology
Simon A. Keek, Manon Beuque, Sergey Primakov, Henry C. Woodruff, Avishek Chatterjee, Janita E. van Timmeren, Martin Vallieres, Lizza E. L. Hendriks, Johannes Kraft, Nicolaus Andratschke, Steve E. Braunstein, Olivier Morin, Philippe Lambin
Summary: Machine learning models based on radiomics and DL features extracted from BM, combined with patient characteristics, have the potential to predict the risk of adverse radiation effects (ARE) at both patient and lesion levels. These models could be valuable in clinical decision making, informing patients about their ARE risk, and guiding physicians towards different therapies.
FRONTIERS IN ONCOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Fredrik Kalholm, Leszek Grzanka, Iuliana Toma-Dasu, Niels Bassler
Summary: This study compares the predictive ability of different input variables for proton RBE models and finds that Q or Q(eff) can better predict the RBE of protons compared to dose-averaged LET.
Article
Radiology, Nuclear Medicine & Medical Imaging
Nils Olofsson, Kenneth Wikstrom, Anna Flejmer, Anders Ahnesjo, Alexandru Dasu
Summary: In lung tumor radiotherapy, the impact of respiratory motion on dose delivery was evaluated using free-breathing images with different planning methods, indicating that RB4 method is recommended for planning of free-breathing treatments. Despite larger tumor motion amplitudes in free-breathing images than in 4DCT, respiratory motion not covered by 4DCT had minimal impact on dose delivery. No consistent correlation was found between dose degradation and patient or motion attributes.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2022)
Review
Oncology
Erich P. Huang, James P. B. O'Connor, Lisa M. McShane, Maryellen L. Giger, Philippe Lambin, Paul E. Kinahan, Eliot L. Siegel, Lalitha K. Shankar
Summary: Integration of computer-extracted tumour characteristics into medical imaging computer-aided diagnosis (CAD) algorithms has been a long-standing practice. However, the translation of radiomics, an extension of CAD involving quantitative characterization of healthy or pathological structures and processes captured by medical imaging, into clinically useful tools has been limited. This may be due to factors such as varying imaging and radiomic feature extraction protocols, potential pitfalls in radiomic data analysis, and a lack of evidence demonstrating the clinical benefit of radiomic-based tools. The authors provide 16 criteria to guide the clinical translation of radiomics, with the aim of accelerating the use of this technology to improve patient outcomes.
NATURE REVIEWS CLINICAL ONCOLOGY
(2023)
Article
Clinical Neurology
Constantin Tuleasca, Iuliana Toma-Dasu, Sebastien Duroux, Daniele Starnoni, Mercy George, Raphael Maire, Roy Thomas Daniel, David Patin, Luis Schiappacasse, Alexandru Dasu, Mohamed Faouzi, Marc Levivier
Summary: This study evaluated the relationship between time and the risk of hearing decline after stereotactic radiosurgery for vestibular schwannomas. The results showed that the risk was associated with sex, dose rate, integral dose, beam-on time, and biologically effective dose.
Article
Oncology
Abdalla Ibrahim, Akshayaa Vaidyanathan, Sergey Primakov, Flore Belmans, Fabio Bottari, Turkey Refaee, Pierre Lovinfosse, Alexandre Jadoul, Celine Derwael, Fabian Hertel, Henry C. C. Woodruff, Helle D. D. Zacho, Sean Walsh, Wim Vos, Mariaelena Occhipinti, Francois-Xavier Hanin, Philippe Lambin, Felix M. M. Mottaghy, Roland Hustinx
Summary: The study aims to develop a deep learning (DL) algorithm for classifying areas of increased uptake on bone scintigraphy scans. The algorithm was trained and validated on a dataset of 2365 scans, and its performance was evaluated on an external testing set of 998 scans. The results showed that the DL algorithm achieved higher specificity and sensitivity compared to nuclear medicine physicians, and it can detect metastatic bone disease (MBD) in a shorter time.
Editorial Material
Oncology
Beatriz Sanchez-Nieto, Liliana Stolarczyk, Alexandru Dasu, Wayne D. Newhauser, Francisco Sanchez-Doblado
FRONTIERS IN ONCOLOGY
(2022)
Review
Dermatology
Y. Widaatalla, T. Wolswijk, F. Adan, L. M. Hillen, H. C. Woodruff, I. Halilaj, A. Ibrahim, P. Lambin, K. Mosterd
Summary: Basal cell carcinoma (BCC) is a common type of cancer, and the application of artificial intelligence techniques for detecting and classifying BCC is necessary. This article reviews the current evidence on the use of handcrafted and deep radiomics models for BCC detection and classification in dermoscopy, optical coherence tomography, and reflectance confocal microscopy.
JOURNAL OF THE EUROPEAN ACADEMY OF DERMATOLOGY AND VENEREOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Manon P. L. Beuque, Marc B. I. Lobbes, Yvonka van Wijk, Yousif Widaatalla, Sergey Primakov, Michael Majer, Corinne Balleyguier, Henry C. Woodruff, Philippe Lambin
Summary: This study developed a comprehensive machine learning tool that can automatically identify, segment, and classify breast lesions on contrast-enhanced mammography (CEM) images in recall patients. The results showed that the combined output of the handcrafted radiomics and deep learning models achieved good diagnostic performance in lesion identification and classification, outperforming the individual models.
Article
Oncology
Zohaib Salahuddin, Yi Chen, Xian Zhong, Henry C. Woodruff, Nastaran Mohammadian Rad, Shruti Atul Mali, Philippe Lambin
Summary: Automatic delineation and detection of primary tumour (GTVp) and lymph nodes (GTVn) in head and neck cancer using PET and CT can help diagnose and stratify patient risk. This study utilized data from nine centres and developed a segmentation model that estimated uncertainty for false positive reduction. Radiomics features extracted from GTVp and GTVn in PET and CT were found to be prognostic for recurrence-free survival prediction. The framework incorporated uncertainty estimation, fairness, and explainability.
Article
Imaging Science & Photographic Technology
Rihab Hami, Sena Apeke, Pascal Redou, Laurent Gaubert, Ludwig J. Dubois, Philippe Lambin, Dimitris Visvikis, Nicolas Boussion
Summary: The effectiveness of radiotherapy depends on various factors, and the tumor response to radiation varies among patients. This study developed a multi-scale model that integrated five major biological concepts in radiotherapy to predict the effects of radiation on tumor growth. The model considered factors such as oxygen level, cell cycle position, cellular sensitivity, and repair, providing a basis for a more personalized clinical tool.
JOURNAL OF IMAGING
(2023)
Article
Oncology
Xian Zhong, Zohaib Salahuddin, Yi Chen, Henry C. Woodruff, Haiyi Long, Jianyun Peng, Xiaoyan Xie, Manxia Lin, Philippe Lambin
Summary: This study developed and validated an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for predicting symptomatic post-hepatectomy liver failure in patients with hepatocellular carcinoma. The clinical-radiomics model outperformed the clinical model and radiomics model, and the first-order radiomics features were identified as the most important for PHLF prediction.
Review
Clinical Neurology
Antonio Santacroce, Mioara-Florentina Trandafirescu, Marc Levivier, David Peters, Christoph Fuerweger, Iuliana Toma-Dasu, Mercy George, Roy Thomas Daniel, Raphael Maire, Makoto Nakamura, Mohamed Faouzi, Luis Schiappacasse, Alexandru Dasu, Constantin Tuleasca
Summary: This study conducted a systematic review and meta-analysis of proton beam therapy for vestibular schwannomas (VSs). The results showed that proton beam therapy achieved a high tumor control rate of 95.4%, but had a lower preservation rate for facial nerve and hearing compared to stereotactic radiosurgery (SRS) techniques.
NEUROSURGICAL REVIEW
(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)