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Ammirabile A, Mastroleo F, Marvaso G, Alterio D, Franzese C, Scorsetti M, Franco P, Giannitto C, Jereczek-Fossa BA. Mapping the research landscape of HPV-positive oropharyngeal cancer: a bibliometric analysis. Crit Rev Oncol Hematol 2024; 196:104318. [PMID: 38431241 DOI: 10.1016/j.critrevonc.2024.104318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024] Open
Abstract
OBJECTIVE The aim of the study is to evaluate the scientific interest, the collaboration patterns and the emerging trends regarding HPV+ OPSCC diagnosis and treatment. MATERIALS AND METHODS A cross-sectional bibliometric analysis of articles reporting on HPV+ OPSCC within Scopus database was performed and all documents published up to December 31th, 2022 were eligible for analysis. Outcomes included the exploration of key characteristics (number of manuscripts published per year, growth rate, top productive countries, most highly cited papers, and the most well-represented journals), collaboration parameters (international collaboration ratio and networks, co-occurrence networks), keywords analysis (trend topics, factorial analysis). RESULTS A total of 5200 documents were found, published from March, 1987 to December, 2022. The number of publications increased annually with an average growth rate of 19.94%, reaching a peak of 680 documents published in 2021. The 10 most cited documents (range 1105-4645) were published from 2000 to 2012. The keywords factorial analysis revealed two main clusters: one on epidemiology, diagnosis, prevention and association with other HPV tumors; the other one about the therapeutic options. According to the frequency of keywords, new items are emerging in the last three years regarding the application of Artifical Intelligence (machine learning and radiomics) and the diagnostic biomarkers (circulating tumor DNA). CONCLUSIONS This bibliometric analysis highlights the importance of research efforts in prevention, diagnostics, and treatment strategies for this disease. Given the urgency of optimizing treatment and improving clinical outcomes, further clinical trials are needed to bridge unaddressed gaps in the management of HPV+ OPSCC patients.
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Affiliation(s)
- Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Milan, Rozzano 20089, Italy
| | - Federico Mastroleo
- Department of Translational Medicine (DIMET), University of Eastern Piedmont and 'Maggiore della Carità' University Hospital, Novara, Italy; Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
| | - Daniela Alterio
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Radiotherapy and Radiosurgery Department, IRCSS Humanitas Research Hospital, Milan, Rozzano, Italy
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Radiotherapy and Radiosurgery Department, IRCSS Humanitas Research Hospital, Milan, Rozzano, Italy
| | - Pierfrancesco Franco
- Department of Translational Medicine (DIMET), University of Eastern Piedmont and 'Maggiore della Carità' University Hospital, Novara, Italy
| | - Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20090, Italy; Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Milan, Rozzano 20089, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Kong Y, Su M, Zhu Y, Li X, Zhang J, Gu W, Yang F, Zhou J, Ni J, Yang X, Zhu Z, Huang J. Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study. Strahlenther Onkol 2024:10.1007/s00066-024-02221-x. [PMID: 38498173 DOI: 10.1007/s00066-024-02221-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/25/2024] [Indexed: 03/20/2024]
Abstract
OBJECTIVE This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients. MATERIALS AND METHODS The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value. RESULTS The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05). CONCLUSION Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients. CLINICAL RELEVANCE STATEMENT Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.
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Affiliation(s)
- Yan Kong
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Mingming Su
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China
| | - Yan Zhu
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Xuan Li
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China
| | - Jinmeng Zhang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, 305-8577, Ibaraki, Japan
| | - Fei Yang
- Department of Radiation Oncology, University of Miami, 33136, Miami, FL, USA
| | - Jialiang Zhou
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Jianjiao Ni
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Xi Yang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Zhengfei Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
| | - Jianfeng Huang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
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Wu KC, Chen SW, Hsieh TC, Yen KY, Chang CJ, Kuo YC, Chang RF, Chia-Hung K. Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography. Nucl Med Commun 2024; 45:196-202. [PMID: 38165173 DOI: 10.1097/mnm.0000000000001799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
OBJECTIVES A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer. METHODS This study used baseline [18F]FDG-PET/CT images of newly diagnosed uterine cervical cancer patients. Data from 186 to 25 patients were analyzed for training and validation cohort, respectively. All patients received chemoradiotherapy (CRT) and follow-up. PET and CT images were augmented by using three-dimensional techniques. The proposed model employed DL to predict distant metastases. Receiver operating characteristic (ROC) curve analysis was performed to measure the model's predictive performance. RESULTS The area under the ROC curves of the training and validation cohorts were 0.818 and 0.830 for predicting distant metastasis, respectively. In the training cohort, the sensitivity, specificity, and accuracy were 80.0%, 78.0%, and 78.5%, whereas, the sensitivity, specificity, and accuracy for distant failure were 73.3%, 75.5%, and 75.2% in the validation cohort, respectively. CONCLUSION Through the use of baseline [ 18 F]FDG-PET/CT images, the proposed DL model can predict the development of distant metastases for patients with locally advanced uterine cervical cancer treatment by CRT. External validation must be conducted to determine the model's predictive performance.
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Affiliation(s)
- Kuo-Chen Wu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei
- Artificial Intelligence Center, China Medical University Hospital
- Department of Radiation Oncology, China Medical University Hospital
| | - Shang-Wen Chen
- Artificial Intelligence Center, China Medical University Hospital
- School of Medicine, College of Medicine, China Medical University, Taichung
- School of Medicine, College of Medicine, Taipei Medical University, Taipei
- Department of Radiation Oncology, China Medical University Hospital
| | - Te-Chun Hsieh
- Department of Nuclear Medicine and PET Center, China Medical University Hospital
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung
| | - Kuo-Yang Yen
- Department of Nuclear Medicine and PET Center, China Medical University Hospital
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung
| | - Chao-Jen Chang
- Artificial Intelligence Center, China Medical University Hospital
| | - Yu-Chieh Kuo
- Artificial Intelligence Center, China Medical University Hospital
| | - Ruey-Feng Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei
- Artificial Intelligence Center, China Medical University Hospital
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei
| | - Kao Chia-Hung
- Artificial Intelligence Center, China Medical University Hospital
- Department of Nuclear Medicine and PET Center, China Medical University Hospital
- Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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Bicci E, Calamandrei L, Mungai F, Granata V, Fusco R, De Muzio F, Bonasera L, Miele V. Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date. Infect Agent Cancer 2023; 18:58. [PMID: 37814320 PMCID: PMC10563217 DOI: 10.1186/s13027-023-00530-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 10/11/2023] Open
Abstract
The tumours of head and neck district are around 3% of all malignancies and squamous cell carcinoma is the most frequent histotype, with rapid increase during the last two decades because of the increment of the infection due to human papilloma virus (HPV). Even if the gold standard for the diagnosis is histological examination, including the detection of viral DNA and transcription products, imaging plays a fundamental role in the detection and staging of HPV + tumours, in order to assess the primary tumour, to establish the extent of disease and for follow-up. The main diagnostic tools are Computed Tomography (CT), Positron Emission Tomography-Computed Tomography (PET-CT) and Magnetic Resonance Imaging (MRI), but also Ultrasound (US) and the use of innovative techniques such as Radiomics have an important role. Aim of our review is to illustrate the main imaging features of HPV + tumours of the oropharynx, in US, CT and MRI imaging. In particular, we will outline the main limitations and strengths of the various imaging techniques, the main uses in the diagnosis, staging and follow-up of disease and the fundamental differential diagnoses of this type of tumour. Finally, we will focus on the innovative technique of texture analysis, which is increasingly gaining importance as a diagnostic tool in aid of the radiologist.
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Affiliation(s)
- Eleonora Bicci
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy.
| | - Leonardo Calamandrei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Naples, 80013, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, 20122, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, Campobasso, 86100, Italy
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vittorio Miele
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
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Ma B, Guo J, Chu H, van Dijk LV, van Ooijen PM, Langendijk JA, Both S, Sijtsema NM. Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer. Phys Imaging Radiat Oncol 2023; 28:100502. [PMID: 38026084 PMCID: PMC10663809 DOI: 10.1016/j.phro.2023.100502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background and purpose To compare the prediction performance of image features of computed tomography (CT) images extracted by radiomics, self-supervised learning and end-to-end deep learning for local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), tumor-specific survival (TSS), overall survival (OS) and disease-free survival (DFS) of oropharyngeal squamous cell carcinoma (OPSCC) patients after (chemo)radiotherapy. Methods and materials The OPC-Radiomics dataset was used for model development and independent internal testing and the UMCG-OPC set for external testing. Image features were extracted from the Gross Tumor Volume contours of the primary tumor (GTVt) regions in CT scans when using radiomics or a self-supervised learning-based method (autoencoder). Clinical and combined (radiomics, autoencoder or end-to-end) models were built using multivariable Cox proportional-hazard analysis with clinical features only and both clinical and image features for LC, RC, LRC, DMFS, TSS, OS and DFS prediction, respectively. Results In the internal test set, combined autoencoder models performed better than clinical models and combined radiomics models for LC, RC, LRC, DMFS, TSS and DFS prediction (largest improvements in C-index: 0.91 vs. 0.76 in RC and 0.74 vs. 0.60 in DMFS). In the external test set, combined radiomics models performed better than clinical and combined autoencoder models for all endpoints (largest improvements in LC, 0.82 vs. 0.71). Furthermore, combined models performed better in risk stratification than clinical models and showed good calibration for most endpoints. Conclusions Image features extracted using self-supervised learning showed best internal prediction performance while radiomics features have better external generalizability.
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Affiliation(s)
- Baoqiang Ma
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Jiapan Guo
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), Groningen, Netherlands
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence , University of Groningen, Groningen, Netherlands
| | - Hung Chu
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), Groningen, Netherlands
- Center for Information Technology, University of Groningen ,Groningen, Netherlands
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Peter M.A. van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), Groningen, Netherlands
| | - Johannes A. Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Nanna M. Sijtsema
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
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Oyouni AAA. Human papillomavirus in cancer: Infection, disease transmission, and progress in vaccines. J Infect Public Health 2023; 16:626-631. [PMID: 36868166 DOI: 10.1016/j.jiph.2023.02.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/04/2023] [Accepted: 02/15/2023] [Indexed: 02/23/2023] Open
Abstract
Human papillomaviruses (HPVs) infect epithelial cells of human beings, and their replication cycle is associated with epithelial differentiation. More than 200 genotypes of HPVs were identified, and each of these HPVs shows distinct specificity for tissues and infection. HPV infection was involved in the development of lesions on the feet, genital warts and hands. The evidence of HPV infection revealed the role of HPVs in neck and head squamous cell carcinoma, esophageal cancer, cervical cancer, head and neck cancer, brain and lung tumours. The independent traditional risk factors, various clinical outcomes, and increased prevalence among certain populations and geographical regions have led increasing interest in HPV infection. The mode of HPVs transmission remains unclear. Moreover, in recent years, vertical transmission of HPVs was reported. This review concludes present knowledge about HPV infection, virulence strains, clinical significance of HPVs, and mode of transmission, and vaccination strategies.
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Affiliation(s)
- Atif Abdulwahab A Oyouni
- Department of Biology, Faculty of Sciences, University of Tabuk, Tabuk, Kingdom of Saudi Arabia; Genome and Biotechnology Unit, Faculty of Sciences, University of Tabuk, Tabuk, Kingdom of Saudi Arabia.
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Bang C, Bernard G, Le WT, Lalonde A, Kadoury S, Bahig H. Artificial intelligence to predict outcomes of head and neck radiotherapy. Clin Transl Radiat Oncol 2023; 39:100590. [PMID: 36935854 PMCID: PMC10014342 DOI: 10.1016/j.ctro.2023.100590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.
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Key Words
- ADASYN, adaptive synthetic sampling
- AI, artificial intelligence
- ANN, artificial neural network
- AUC, Area Under the ROC Curve
- Artificial intelligence
- BMI, body mass index
- C-Index, concordance index
- CART, Classification and Regression Tree
- CBCT, cone-beam computed tomography
- CIFE, conditional informax feature extraction
- CNN, convolutional neural network
- CRT, chemoradiation
- CT, computed tomography
- Cancer outcomes
- DL, deep learning
- DM, distant metastasis
- DSC, Dice Similarity Coefficient
- DSS, clinical decision support systems
- DT, Decision Tree
- DVH, Dose-volume histogram
- GANs, Generative Adversarial Networks
- GB, Gradient boosting
- GPU, graphical process units
- HNC, head and neck cancer
- HPV, human papillomavirus
- HR, hazard ratio
- Head and neck cancer
- IAMB, incremental association Markov blanket
- IBDM, image based data mining
- IBMs, image biomarkers
- IMRT, intensity-modulated RT
- KNN, k nearest neighbor
- LLR, Local linear forest
- LR, logistic regression
- LRR, loco-regional recurrence
- MIFS, mutual information based feature selection
- ML, machine learning
- MRI, Magnetic resonance imaging
- MRMR, Minimum redundancy feature selection
- Machine learning
- N-MLTR, Neural Multi-Task Logistic Regression
- NPC, nasopharynx
- NTCP, Normal Tissue Complication Probability
- OPC, oropharyngeal cancer
- ORN, osteoradionecrosis
- OS, overall survival
- PCA, Principal component analysis
- PET, Positron emission tomography
- PG, parotid glands
- PLR, Positive likelihood ratio
- PM, pharyngeal mucosa
- PTV, Planning target volumes
- PreSANet, deep preprocessor module and self-attention
- Predictive modeling
- QUANTEC, Quantitative Analyses of Normal Tissue Effects in the Clinic
- RF, random forest
- RFC, random forest classifier
- RFS, recurrence free survival
- RLR, Rigid logistic regression
- RRF, Regularized random forest
- RSF, random survival forest
- RT, radiotherapy
- RTLI, radiation-induced temporal lobe injury
- Radiomic
- SDM, shared decision making
- SMG, submandibular glands
- SMOTE, synthetic minority over-sampling technique
- STIC, sticky saliva
- SVC, support vector classifier
- SVM, support vector machine
- XGBoost, extreme gradient boosting
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Affiliation(s)
- Chulmin Bang
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Corresponding author at: Centre Hospitalier de l'Université de Montréal, 3840 Rue Saint-Urbain, Montréal, QC H2W 1T8, Canada.
| | - Galaad Bernard
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
| | - William T. Le
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Arthur Lalonde
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Université de Montréal, Montreal, QC, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Houda Bahig
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
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Caprini E, D'Agnese G, Brennan PA, Rahimi S. Human papilomaviru-related oropharyngeal squamous cell carcinoma and radiomics: A new era? J Oral Pathol Med 2023; 52:300-304. [PMID: 36847112 DOI: 10.1111/jop.13419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/21/2023] [Indexed: 03/01/2023]
Abstract
BACKGROUND The increase of the incidence of human papillomavirus dependent oropharyngeal squamous cell carcinoma is alarming, although we have greatly progressed in the classification and staging of this disease. We now know that human papillomavirus related oropharyngeal squamous cell carcinoma is a sub-type of head and neck squamous cell carcinoma with favourable prognosis and good response to therapy that needs a proper system of classification and staging. Thus, in routine practice it is essential to test patients for the presence of human papillomavirus. The most popular technique to assess human papillomavirus status is immunohistochemistry on biopsy samples with p16, which is an excellent surrogate for high-risk human papillomavirus infection. Another highly sensitive and specific tissue-based technique for the detection of human papillomavirus is RNAscope In situ hybridization that has a prohibitive cost, limiting its use in routine practice. Radiomics is an artificial intelligence based non-invasive method of computational analysis of computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound images. METHODS In this review, we summarise the last findings of radiomics applied to human papillomavirus associated oropharyngeal squamous cell carcinoma. RESULTS A growing body of evidence suggest that radiomics is able to characterise and detect early relapse after treatment, and enable development of tailored therapy of human papillomavirus positive oropharyngeal squamous cell carcinoma.
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Affiliation(s)
- Elisabetta Caprini
- Anatomia Patologica, Istituto Dermopatico dell'Immacolata IRCCS, Rome, Italy
| | - Giampaolo D'Agnese
- Information Technology, Istituto Dermopatico dell'immacolata IDI-IRCCS, Rome, Italy
| | - Peter A Brennan
- Department of Oral and Maxillofacial Surgery, Queen Alexandra Hospital, Portsmouth, UK
| | - Siavash Rahimi
- Anatomia Patologica, Istituto Dermopatico dell'Immacolata IRCCS, Rome, Italy
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Predictive Value of 18F-Fluorodeoxyglucose Positron-Emission Tomography Metabolic and Volumetric Parameters for Systemic Metastasis in Tonsillar Cancer. Cancers (Basel) 2022; 14:cancers14246242. [PMID: 36551727 PMCID: PMC9777518 DOI: 10.3390/cancers14246242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Although the prognosis of tonsillar cancer (human papillomavirus-positive oropharyngeal squamous cell carcinoma) is improving, disease control failure (distant metastasis) still occurs in some cases. We explored whether several 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) parameters can predict metastasis. We retrospectively reviewed the medical records of 55 patients with tonsil squamous cell carcinoma who underwent pretreatment 18F-FDG positron-emission tomography/computed tomography (PET/CT) followed by primary surgery. During the follow-up period, systemic metastases were found in 7 of the 55 patients. The most common sites were the lungs (33%), bone (22%), brain/skull base (22%), small bowel (11%), and liver (11%). Pathologically, P53 mutation was less common in patients with systemic metastasis (41.7% vs. 14.3%, p = 0.054) than without systemic metastasis. In terms of PET parameters, the metabolic tumor volume (MTV2.5) and total lesion glycolysis (TLG2.5) values were lower in the primary tumor, and higher in the metastatic lymph nodes, of human papillomavirus (HPV)-positive compared to HPV-negative patients (all p < 0.05). The MTV2.5, TLG2.5, and tumor−to−liver uptake ratio were 36.07 ± 54.24 cm3, 183.46 ± 298.62, and 4.90 ± 2.77, respectively, in the systemic metastasis group, respectively; all of these values were higher than those of the patients without systemic metastasis (all p < 0.05). The MTV2.5 value was significantly different between the groups even when the values for the primary tumor and metastatic lymph nodes were summed (53.53 ± 57.78 cm3, p = 0.036). The cut-off value, area under the curve (95% confidence interval), sensitivity, and specificity of MTV2.5 for predicting systemic metastasis were 11.250 cm3, 0.584 (0.036−0.832), 0.571, and 0.565, respectively. The MTV2.5 of metastatic lymph nodes and summed MTV2.5 values of the primary tumor and metastatic lymph nodes were significantly higher in tonsillar cancer patients with than without systemic metastases. We suggest PET/CT scanning for pre-treatment cancer work-up and post-treatment surveillance to consider additional systemic therapy in patients with a high risk of disease control failure.
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Rich BJ, Spieler BO, Yang Y, Young L, Amestoy W, Monterroso M, Wang L, Dal Pra A, Yang F. Erring Characteristics of Deformable Image Registration-Based Auto-Propagation for Internal Target Volume in Radiotherapy of Locally Advanced Non-Small Cell Lung Cancer. Front Oncol 2022; 12:929727. [PMID: 35936742 PMCID: PMC9353179 DOI: 10.3389/fonc.2022.929727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeRespiratory motion of locally advanced non-small cell lung cancer (LA-NSCLC) adds to the challenge of targeting the disease with radiotherapy (RT). One technique used frequently to alleviate this challenge is an internal gross tumor volume (IGTV) generated from manual contours on a single respiratory phase of the 4DCT via the aid of deformable image registration (DIR)-based auto-propagation. Through assessing the accuracy of DIR-based auto-propagation for generating IGTVs, this study aimed to identify erring characteristics associated with the process to enhance RT targeting in LA-NSCLC.Methods4DCTs of 19 patients with LA-NSCLC were acquired using retrospective gating with 10 respiratory phases (RPs). Ground-truth IGTVs (GT-IGTVs) were obtained through manual segmentation and union of gross tumor volumes (GTVs) in all 10 phases. IGTV auto-propagation was carried out using two distinct DIR algorithms for the manually contoured GTV from each of the 10 phases, resulting in 10 separate IGTVs for each patient per each algorithm. Differences between the auto-propagated IGTVs (AP-IGTVs) and their corresponding GT-IGTVs were assessed using Dice coefficient (DICE), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), and percent volume difference (PVD) and further examined in relation to anatomical tumor location, RP, and deformation index (DI) that measures the degree of deformation during auto-propagation. Furthermore, dosimetric implications due to the analyzed differences between the AP-IGTVs and GT-IGTVs were assessed.ResultsFindings were largely consistent between the two algorithms: DICE, MSSD, ASSD, and PVD showed no significant differences between the 10 RPs used for propagation (Kruskal–Wallis test, ps > 0.90); MSSD and ASSD differed significantly by tumor location in the central–peripheral and superior–inferior dimensions (ps < 0.0001) while only in the central–peripheral dimension for PVD (p < 0.001); DICE, MSSD, and ASSD significantly correlated with the DI (Spearman’s rank correlation test, ps < 0.0001). Dosimetric assessment demonstrated that 79% of the radiotherapy plans created by targeting planning target volumes (PTVs) derived from the AP-IGTVs failed prescription constraints for their corresponding ground-truth PTVs.ConclusionIn LA-NSCLC, errors in DIR-based IGTV propagation present to varying degrees and manifest dependences on DI and anatomical tumor location, indicating the need for personalized consideration in designing RT internal target volume.
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Affiliation(s)
- Benjamin J. Rich
- Department of Radiation Oncology, University of Miami, Miami, FL, United States
| | - Benjamin O. Spieler
- Department of Radiation Oncology, University of Miami, Miami, FL, United States
| | - Yidong Yang
- Department of Radiation Oncology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Lori Young
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - William Amestoy
- Department of Radiation Oncology, University of Miami, Miami, FL, United States
| | - Maria Monterroso
- Department of Radiation Oncology, University of Miami, Miami, FL, United States
| | - Lora Wang
- Department of Radiation Oncology, University of Miami, Miami, FL, United States
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami, Miami, FL, United States
| | - Fei Yang
- Department of Radiation Oncology, University of Miami, Miami, FL, United States
- *Correspondence: Fei Yang,
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature. Cancers (Basel) 2022; 14:cancers14102445. [PMID: 35626048 PMCID: PMC9139172 DOI: 10.3390/cancers14102445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The incidence of squamous cell carcinomas of the oropharynx has rapidly increased in the last two decades due to human papilloma virus infection (HPV). HPV-positive and HPV-negative squamous cell tumours differ in radiological imaging, treatment, and prognosis; therefore, differential diagnosis is mandatory. Radiomics with texture analysis is an innovative technique that has been used increasingly in recent years to characterise the tissue heterogeneity of certain structures such as neoplasms or organs by measuring the spatial distribution of pixel values on radiological imaging. This review delineates the application of texture analysis in oropharyngeal tumours and explores how radiomics may potentially improve clinical decision-making. Abstract Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.
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