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Toosi A, Shiri I, Zaidi H, Rahmim A. Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs). Cancers (Basel) 2024; 16:2538. [PMID: 39061178 PMCID: PMC11274485 DOI: 10.3390/cancers16142538] [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: 05/31/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released.
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Affiliation(s)
- Amirhosein Toosi
- Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada;
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Isaac Shiri
- Department of Cardiology, University Hospital Bern, CH-3010 Bern, Switzerland;
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland;
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland;
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada;
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
- Department of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
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Lamba M, Singh PR, Bandyopadhyay A, Goswami A. Synthetic 18F labeled biomolecules that are selective and promising for PET imaging: major advances and applications. RSC Med Chem 2024; 15:1899-1920. [PMID: 38911154 PMCID: PMC11187557 DOI: 10.1039/d4md00033a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/14/2024] [Indexed: 06/25/2024] Open
Abstract
The concept of positron emission tomography (PET) based imaging was developed more than 40 years ago. It has been a widely adopted technique for detecting and staging numerous diseases in clinical settings, particularly cancer, neuro- and cardio-diseases. Here, we reviewed the evolution of PET and its advantages over other imaging modalities in clinical settings. Primarily, this review discusses recent advances in the synthesis of 18F radiolabeled biomolecules in light of the widely accepted performance for effective PET. The discussion particularly emphasizes the 18F-labeling chemistry of carbohydrates, lipids, amino acids, oligonucleotides, peptides, and protein molecules, which have shown promise for PET imaging in recent decades. In addition, we have deliberated on how 18F-labeled biomolecules enable the detection of metabolic changes at the cellular level and the selective imaging of gross anatomical localization via PET imaging. In the end, the review discusses the future perspective of PET imaging to control disease in clinical settings. We firmly believe that collaborative multidisciplinary research will further widen the comprehensive applications of PET approaches in the clinical management of cancer and other pathological outcomes.
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Affiliation(s)
- Manisha Lamba
- Department of Chemistry, Indian Institute of Technology Birla Farms Ropar Punjab-140001 India
| | - Prasoon Raj Singh
- Department of Chemistry, Indian Institute of Technology Birla Farms Ropar Punjab-140001 India
| | - Anupam Bandyopadhyay
- Department of Chemistry, Indian Institute of Technology Birla Farms Ropar Punjab-140001 India
| | - Avijit Goswami
- Department of Chemistry, Indian Institute of Technology Birla Farms Ropar Punjab-140001 India
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Philip MM, Welch A, McKiddie F, Nath M. A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients. Cancer Med 2023; 12:16181-16194. [PMID: 37353996 PMCID: PMC10469753 DOI: 10.1002/cam4.6278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/07/2023] [Accepted: 06/11/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Positron emission tomography (PET) images of head and neck squamous cell carcinoma (HNSCC) patients can assess the functional and biochemical processes at cellular levels. Therefore, PET radiomics-based prediction and prognostic models have the potentials to understand tumour heterogeneity and assist clinicians with diagnosis, prognosis and management of the disease. We conducted a systematic review of published modelling information to evaluate the usefulness of PET radiomics in the prediction and prognosis of HNSCC patients. METHODS We searched bibliographic databases (MEDLINE, Embase, Web of Science) from 2010 to 2021 and considered 31 studies with pre-defined inclusion criteria. We followed the CHARMS checklist for data extraction and performed quality assessment using the PROBAST tool. We conducted a meta-analysis to estimate the accuracy of the prediction and prognostic models using the diagnostic odds ratio (DOR) and average C-statistic, respectively. RESULTS Manual segmentation method followed by 40% of the maximum standardised uptake value (SUVmax ) thresholding is a commonly used approach. The area under the receiver operating curves of externally validated prediction models ranged between 0.60-0.87, 0.65-0.86 and 0.62-0.75 for overall survival, distant metastasis and recurrence, respectively. Most studies highlighted an overall high risk of bias (outcome definition, statistical methodologies and external validation of models) and high unclear concern in terms of applicability. The meta-analysis showed the estimated pooled DOR of 6.75 (95% CI: 4.45, 10.23) for prediction models and the C-statistic of 0.71 (95% CI: 0.67, 0.74) for prognostic models. CONCLUSIONS Both prediction and prognostic models using clinical variables and PET radiomics demonstrated reliable accuracy for detecting adverse outcomes in HNSCC, suggesting the prospect of PET radiomics in clinical settings for diagnosis, prognosis and management of HNSCC patients. Future studies of prediction and prognostic models should emphasise the quality of reporting, external model validation, generalisability to real clinical scenarios and enhanced reproducibility of results.
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Affiliation(s)
| | - Andy Welch
- Institute of Education in Healthcare and Medical Sciences, University of AberdeenAberdeenUK
| | | | - Mintu Nath
- Institute of Applied Health Sciences, University of AberdeenAberdeenUK
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Sun MX, Zhao MJ, Zhao LH, Jiang HR, Duan YX, Li G. A nomogram model based on pre-treatment and post-treatment MR imaging radiomics signatures: application to predict progression-free survival for nasopharyngeal carcinoma. Radiat Oncol 2023; 18:67. [PMID: 37041545 PMCID: PMC10088158 DOI: 10.1186/s13014-023-02257-w] [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: 10/13/2022] [Accepted: 04/03/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND To establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II-IVA nasopharyngeal carcinoma (NPC) in South China. METHODS One hundred and twenty NPC patients who underwent chemoradiotherapy were enrolled (80 in the training cohort and 40 in the validation cohort). Acquiring data and screening features were performed successively. Totally 1133 radiomics features were extracted from the T2-weight images before and after treatment. Least absolute shrinkage and selection operator regression, recursive feature elimination algorithm, random forest, and minimum-redundancy maximum-relevancy (mRMR) method were used for feature selection. Nomogram discrimination and calibration were evaluated. Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analyses were applied to appraise the prognostic performance of nomograms. Survival curves were plotted using Kaplan-Meier method. RESULTS Integrating independent clinical predictors with pre-treatment and post-treatment radiomics signatures which were calculated in conformity with radiomics features, we established a clinical-and-radiomics nomogram by multivariable Cox regression. Nomogram consisting of 14 pre-treatment and 7 post-treatment selected features has been proved to yield a reliable predictive performance in both training and validation groups. The C-index of clinical-and-radiomics nomogram was 0.953 (all P < 0.05), which was higher than that of clinical (0.861) or radiomics nomograms alone (based on pre-treatment statistics: 0.942; based on post-treatment statistics: 0.944). Moreover, we received Rad-score of pre-treatment named RS1 and post-treatment named RS2 and all were used as independent predictors to divide patients into high-risk and low-risk groups. Kaplan-Meier analysis showed that lower RS1 (less than cutoff value, - 1.488) and RS2 (less than cutoff value, - 0.180) were easier to avoid disease progression (all P < 0.01). It showed clinical benefit with decision curve analysis. CONCLUSIONS MR-based radiomics measured the burden on primary tumor before treatment and the tumor regression after chemoradiotherapy, and was used to build a model to predict progression-free survival (PFS) in the stage II-IVA NPC patients. It can also help to distinguish high-risk patients from low-risk patients, thus guiding personalized treatment decisions effectively.
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Affiliation(s)
- Mi-Xue Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Meng-Jing Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Li-Hao Zhao
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Hao-Ran Jiang
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Yu-Xia Duan
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
| | - Gang Li
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
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Lv W, Xu H, Han X, Zhang H, Ma J, Rahmim A, Lu L. Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer. Cancers (Basel) 2022; 14:cancers14071674. [PMID: 35406449 PMCID: PMC8996849 DOI: 10.3390/cancers14071674] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/19/2022] [Accepted: 03/21/2022] [Indexed: 12/15/2022] Open
Abstract
Simple Summary This study investigated the ability of context-aware saliency-guided PET/CT radiomics in the prediction of outcome and HPV status for head and neck cancer. In total, 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value. This multi-center study highlights the feasibility of saliency-guided PET/CT radiomics in outcome predictions of head and neck cancer, confirming that certain regions are more relevant to tumor aggressiveness and prognosis. Abstract Purpose: This multi-center study aims to investigate the prognostic value of context-aware saliency-guided radiomics in 18F-FDG PET/CT images of head and neck cancer (HNC). Methods: 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). There were 100/384 and 60/123 oropharyngeal carcinoma (OPC) patients with human papillomavirus (HPV) status in training and testing cohorts, respectively. Six types of images were used for radiomics feature extraction and further model construction, namely (i) the original image (Origin), (ii) a context-aware saliency map (SalMap), (iii, iv) high- or low-saliency regions in the original image (highSal or lowSal), (v) a saliency-weighted image (SalxImg), and finally, (vi) a fused PET-CT image (FusedImg). Four outcomes were evaluated, i.e., recurrence-free survival (RFS), metastasis-free survival (MFS), overall survival (OS), and disease-free survival (DFS), respectively. Multivariate Cox analysis and logistic regression were adopted to construct radiomics scores for the prediction of outcome (Rad_Ocm) and HPV-status (Rad_HPV), respectively. Besides, the prognostic value of their integration (Rad_Ocm_HPV) was also investigated. Results: In the external testing cohort, compared with the Origin model, SalMap and SalxImg achieved the highest C-indices for RFS (0.621 vs. 0.559) and MFS (0.785 vs. 0.739) predictions, respectively, while FusedImg performed the best for both OS (0.685 vs. 0.659) and DFS (0.641 vs. 0.582) predictions. In the OPC HPV testing cohort, FusedImg showed higher AUC for HPV-status prediction compared with the Origin model (0.653 vs. 0.484). In the OPC testing cohort, compared with Rad_Ocm or Rad_HPV alone, Rad_Ocm_HPV performed the best for OS and DFS predictions with C-indices of 0.702 (p = 0.002) and 0.684 (p = 0.006), respectively. Conclusion: Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value.
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Affiliation(s)
- Wenbing Lv
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Hui Xu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Xu Han
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Hao Zhang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China;
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada;
- Department of Radiology, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z1, Canada
- Department of Physics, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; (W.L.); (H.X.); (X.H.); (J.M.)
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China
- Pazhou Lab, Guangzhou 510330, China
- Correspondence: ; Tel.: +86-020-62789116
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Benjamin J, Hephzibah J, Shanthly N, Oommen R, Mathew D, Pavamani S, Rajnikanth J. F-18 FDG PET-CT for response evaluation in head and neck malignancy: Experience from a tertiary level hospital in south India. Cancer Rep (Hoboken) 2021; 4:e1333. [PMID: 33660434 PMCID: PMC8222552 DOI: 10.1002/cnr2.1333] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/08/2020] [Accepted: 12/14/2020] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Head and neck squamous cell carcinoma (HNSCC) accounts for 90% of head and neck cancers. There has been no established qualitative system of interpretation for therapy response assessment using PET-CT for HNSCC. AIM To assess response evaluation of nodal status in post-treatment PET-CT scans in HNSCC using a 5-point Likert scale (Deauville score [DS]). METHODS AND RESULTS Retro-prospective analysis was performed of the nodal status of pre and post-RT PET-CT in patients diagnosed with HNSCC (n = 43) from May 2013 to March 2018. All eligible patients underwent a pre-RT PET-CT scan before the start of RT. Another post-RT PET-CT scan was performed 12 weeks after the completion of RT. The median time from completion of radiotherapy (RT) to post-RT PET-CT was 92 days; 80% of the patients had their post-RT PET-CT scan between 77 and 147 days after therapy. Of 43 patients (M/33, F/10, age range 18 to 80 years (median 54 years) selected for the study, good concordance was noted between DS and clinical response in these patients. The change in SUV from pre-RT PET to post-RT PET was analyzed using a paired t-test. The P-value was found to be statistically significant while comparing pre and post-RT SUVmax levels showing that RT had significantly reduced the SUVmax levels of the nodes in DS 2-3 groups whereas the number of patients was too small to allow a reliable calculation in DS 4-5 groups. It was found that 36/39 patients with DS 1-3 had no nodal recurrence showing a high NPV of 92.3%. Of the four patients with DS 4-5, all had active disease showing PPV of 100%. Applying Fisher's exact test, the P-value was found to be .004. CONCLUSION DS seems to satisfy the requirements for a simple qualitative method of interpreting PET scans and for identifying patients requiring neck dissection. Consensus regarding qualitative assessment would facilitate standardization of PET reporting in clinical practice and enable comparative multicentric studies.
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Affiliation(s)
- Justin Benjamin
- Department of Nuclear Medicine, Christian Medical College, Vellore, India
| | - Julie Hephzibah
- Department of Nuclear Medicine, Christian Medical College, Vellore, India
| | | | - Regi Oommen
- Department of Nuclear Medicine, Christian Medical College, Vellore, India
| | - David Mathew
- Department of Nuclear Medicine, Christian Medical College, Vellore, India
| | - Simon Pavamani
- Department of Radiation Oncology, Christian Medical College & Hospital, Vellore, India
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Belgioia L, Morbelli SD, Corvò R. Prediction of Response in Head and Neck Tumor: Focus on Main Hot Topics in Research. Front Oncol 2021; 10:604965. [PMID: 33489911 PMCID: PMC7821385 DOI: 10.3389/fonc.2020.604965] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/26/2020] [Indexed: 11/13/2022] Open
Abstract
Radiation therapy is a cornerstone in the treatment of head and neck cancer patients; actually, their management is based on clinical and radiological staging with all patients at the same stage treated in the same way. Recently the increasing knowledge in molecular characterization of head and neck cancer opens the way for a more tailored treatment. Patient outcomes could be improved by a personalized radiotherapy beyond technological and anatomical precision. Several tumor markers are under evaluation to understand their possible prognostic or predictive value. In this paper we discuss those markers specific for evaluate response to radiation therapy in head and neck cancer for a shift toward a biological personalization of radiotherapy.
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Affiliation(s)
- Liliana Belgioia
- Radiation Oncology Department, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Health Science Department (DISSAL), University of Genoa, Genoa, Italy
| | - Silvia Daniela Morbelli
- Health Science Department (DISSAL), University of Genoa, Genoa, Italy.,Nuclear Medicine Department, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Renzo Corvò
- Radiation Oncology Department, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Health Science Department (DISSAL), University of Genoa, Genoa, Italy
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Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy. PLoS One 2020; 15:e0240043. [PMID: 33017440 PMCID: PMC7535039 DOI: 10.1371/journal.pone.0240043] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 09/18/2020] [Indexed: 01/28/2023] Open
Abstract
Background We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI. Methods A total of 14 T4NxM0 NPC patients with histologically proven “in field” recurrence in the post nasal space following curative intent IMRT were included in this study. Pretreatment MRI were co-registered with MRI at the time of recurrence for the delineation of gross tumor volume at diagnosis(GTV) and at recurrence(GTVr). A total of 7 histogram features and 40 texture features were computed from the recurrent(GTVr) and non-recurrent region(GTV-GTVr). Paired t-tests and Wilcoxon signed-rank tests were carried out on the 47 quantified radiomics features. Results A total of 7 features were significantly different between recurrent and non-recurrent regions. Other than the variance from intensity-based histogram, the remaining six significant features were either from the gray-level size zone matrix (GLSZM) or the neighbourhood gray-tone difference matrix (NGTDM). Conclusions The radiomic features extracted from pre-treatment MRI can potentially reflect the difference between recurrent and non-recurrent regions within a tumor and has a potential role in pre-treatment identification of intra-tumoral radio-resistance for selective dose escalation.
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Shukla M, Forghani R, Agarwal M. Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging. Neuroimaging Clin N Am 2020; 30:341-357. [PMID: 32600635 DOI: 10.1016/j.nic.2020.04.005] [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] [Indexed: 12/24/2022]
Abstract
The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.
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Affiliation(s)
- Monica Shukla
- Department of Radiation Oncology, Froedtert and Medical College of Wisconsin, 9200 W. Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory, Department of Radiology, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada
| | - Mohit Agarwal
- Department of Radiology, Section of Neuroradiology, Froedtert and Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
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Samolyk-Kogaczewska N, Sierko E, Dziemianczyk-Pakiela D, Nowaszewska KB, Lukasik M, Reszec J. Usefulness of Hybrid PET/MRI in Clinical Evaluation of Head and Neck Cancer Patients. Cancers (Basel) 2020; 12:cancers12020511. [PMID: 32098356 PMCID: PMC7072319 DOI: 10.3390/cancers12020511] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/17/2020] [Accepted: 02/17/2020] [Indexed: 12/22/2022] Open
Abstract
(1) Background: The novel hybrid of positron emission tomography/magnetic resonance (PET/MR) examination has been introduced to clinical practice. The aim of our study was to evaluate PET/MR usefulness in preoperative staging of head and neck cancer (HNC) patients (pts); (2) Methods: Thirty eight pts underwent both computed tomography (CT) and PET/MR examination, of whom 21 pts underwent surgical treatment as first-line therapy and were further included in the present study. Postsurgical tissue material was subjected to routine histopathological (HP) examination with additional evaluation of p16, human papillomavirus (HPV), Epstein-Barr virus (EBV) and Ki67 status. Agreement of clinical and pathological T staging, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of CT and PET/MR in metastatic lymph nodes detection were defined. The verification of dependences between standardized uptake value (SUV value), tumor geometrical parameters, number of metastatic lymph nodes in PET/MR and CT, biochemical parameters, Ki67 index, p16, HPV and EBV status was made with statistical analysis of obtained results; (3) Results: PET/MR is characterized by better agreement in T staging, higher specificity, sensitivity, PPV and NPV of lymph nodes evaluation than CT imaging. Significant correlations were observed between SUVmax and maximal tumor diameter from PET/MR, between SUVmean and CT tumor volume, PET/MR tumor volume, maximal tumor diameter assessed in PET/MR. Other correlations were weak and insignificant; (4) Conclusions: Hybrid PET/MR imaging is useful in preoperative staging of HNC. Further studies are needed.
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Affiliation(s)
| | - Ewa Sierko
- Department of Radiotherapy, Comprehensive Cancer Center, 15-027 Bialystok, Poland;
- Department of Oncology, Medical University of Bialystok, 15-027 Bialystok, Poland
- Correspondence: ; Tel.: +48-85-6646827
| | - Dorota Dziemianczyk-Pakiela
- Department of Otolaryngology and Maxillofacial Surgery, Jedrzej Sniadecki Memorial Regional Hospital, 15-950 Bialystok, Poland;
| | - Klaudia Beata Nowaszewska
- Department of Maxillofacial and Plastic Surgery, Medical University of Bialystok, 15-276 Bialystok, Poland;
| | - Malgorzata Lukasik
- Department of Medical Pathology, Medical University of Bialystok, 15-089 Bialystok, Poland; (M.L.); (J.R.)
| | - Joanna Reszec
- Department of Medical Pathology, Medical University of Bialystok, 15-089 Bialystok, Poland; (M.L.); (J.R.)
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Bogowicz M, Tanadini-Lang S, Veit-Haibach P, Pruschy M, Bender S, Sharma A, Hüllner M, Studer G, Stieb S, Hemmatazad H, Glatz S, Guckenberger M, Riesterer O. Perfusion CT radiomics as potential prognostic biomarker in head and neck squamous cell carcinoma. Acta Oncol 2019; 58:1514-1518. [PMID: 31304860 DOI: 10.1080/0284186x.2019.1629013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- M. Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - S. Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - P. Veit-Haibach
- Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Department of Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - M. Pruschy
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - S. Bender
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - A. Sharma
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - M. Hüllner
- Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - G. Studer
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Institute for Radiation Oncology, Cantonal Hospital Lucerne, Lucerne, Switzerland
| | - S. Stieb
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - H. Hemmatazad
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - S. Glatz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - M. Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - O. Riesterer
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Center for Radiation Oncology, KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
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12
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Gardin I, Grégoire V, Gibon D, Kirisli H, Pasquier D, Thariat J, Vera P. Radiomics: Principles and radiotherapy applications. Crit Rev Oncol Hematol 2019; 138:44-50. [PMID: 31092384 DOI: 10.1016/j.critrevonc.2019.03.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 12/26/2018] [Accepted: 03/26/2019] [Indexed: 12/14/2022] Open
Abstract
Radiomics is defined as the extraction of a large quantity of quantitative image features. The different radiomic indexes that have been proposed in the literature are described as well as the various factors that have an impact on the robustness of these indexes. We will see that several hundred quantitative features can be extracted per lesion and imaging modality. The ever-growing number of features studied raises the question of the statistical method of analysis used. This review addresses the research supporting the clinical use of radiomics in oncology in the staging of disease, discrimination between healthy and pathological tissues, the identification of genetic features, the prediction of patient survival, the response to treatment, the recurrence after radiotherapy and chemoradiotherapy and the side effects. Based on the existing literature, it remains difficult to identify features that should be used for current clinical practice.
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Affiliation(s)
- I Gardin
- Department of Nuclear Medicine, Centre Henri-Becquerel, France; LITIS EA4108, Normandie University, Rouen, France.
| | - V Grégoire
- Department of Radiation Oncology, Centre Léon Bérard, France
| | - D Gibon
- Research and Innovation Department, AQUILAB, Loos Les Lille, France
| | - H Kirisli
- Research and Innovation Department, AQUILAB, Loos Les Lille, France
| | - D Pasquier
- Department of Radiation Oncology, Centre Oscar Lambret, CRIStAL UMR CNRS 9189, Lille University, Lille, France
| | - J Thariat
- Radiotherapy Department, Centre François Baclesse, Caen, France
| | - P Vera
- Department of Nuclear Medicine, Centre Henri-Becquerel, France; LITIS EA4108, Normandie University, Rouen, France
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13
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Rusten E, Rekstad BL, Undseth C, Klotz D, Hernes E, Guren MG, Malinen E. Anal cancer chemoradiotherapy outcome prediction using 18F-fluorodeoxyglucose positron emission tomography and clinicopathological factors. Br J Radiol 2019; 92:20181006. [PMID: 30810343 DOI: 10.1259/bjr.20181006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE To assess the role of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET), obtained before and during chemoradiotherapy, in predicting locoregional failure relative to clinicopathological factors for patients with anal cancer. METHODS 93 patients with anal squamous cell carcinoma treated with chemoradiotherapy were included in a prospective observational study (NCT01937780). FDG-PET/CT was performed for all patients before treatment, and for a subgroup (n = 39) also 2 weeks into treatment. FDG-PET was evaluated with standardized uptake values (SUVmax/peak/mean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and a proposed Z-normalized combination of MTV and SUVpeak (ZMP). The objective was to predict locoregional failure using FDG-PET, tumor and lymph node stage, gross tumor volume (GTV) and human papilloma virus (HPV) status in univariate and bivariate Cox regression analysis. RESULTS N3 lymph node stage, HPV negative tumor, GTV, MTV, TLG and ZMP were in univariate analysis significant predictors of locoregional failure (p < 0.01), while SUVmax/peak/mean were not (p > 0.2). In bivariate analysis HPV status was the most independent predictor in combinations with N3 stage, ZMP, TLG, and MTV (p < 0.02). The FDG-PET parameters at 2 weeks into radiotherapy decreased by 30-40 % of the initial values, but neither absolute nor relative decrease improved the prediction models. CONCLUSION Pre-treatment PET parameters are predictive of chemoradiotherapy outcome in anal cancer, although HPV negativity and N3 stage are the strongest single predictors. Predictions can be improved by combining HPV with PET parameters such as MTV, TLG or ZMP. PET 2 weeks into treatment does not provide added predictive value. ADVANCES IN KNOWLEDGE Pre-treatment PET parameters of anal cancer showed a predictive role independent of clinicopathological factors. Although the PET parameters show substantial reduction from pre- to mid-treatment, the changes were not predictive of chemoradiotherapy outcome.
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Affiliation(s)
- Espen Rusten
- 1 Department of Medical Physics, University of Oslo , Oslo , Norway
| | | | | | - Dagmar Klotz
- 3 Department of Pathology, University of Oslo , Oslo , Norway
| | - Eivor Hernes
- 4 Department of Nuclear Medicine, University of Oslo , Oslo , Norway
| | - Marianne Grønlie Guren
- 2 Department of Oncology, University of Oslo , Oslo , Norway.,5 K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital , Oslo , Norway
| | - Eirik Malinen
- 1 Department of Medical Physics, University of Oslo , Oslo , Norway.,6 Department of Physics, University of Oslo , Oslo , Norway
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14
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Molecular Imaging-Guided Radiotherapy for the Treatment of Head-and-Neck Squamous Cell Carcinoma: Does it Fulfill the Promises? Semin Radiat Oncol 2018; 28:35-45. [PMID: 29173754 DOI: 10.1016/j.semradonc.2017.08.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
With the routine use of intensity modulated radiation therapy for the treatment of head-and-neck squamous cell carcinoma allowing highly conformed dose distribution, there is an increasing need for refining both the selection and the delineation of gross tumor volumes (GTV). In this framework, molecular imaging with positron emission tomography and magnetic resonance imaging offers the opportunity to improve diagnostic accuracy and to integrate tumor biology mainly related to the assessment of tumor cell density, tumor hypoxia, and tumor proliferation into the treatment planning equation. Such integration, however, requires a deep comprehension of the technical and methodological issues related to image acquisition, reconstruction, and segmentation. Until now, molecular imaging has had a limited value for the selection of nodal GTV, but there are increasing evidences that both FDG positron emission tomography and diffusion-weighted magnetic resonance imaging has a potential value for the delineation of the primary tumor GTV, effecting on dose distribution. With the apprehension of the heterogeneity in tumor biology through molecular imaging, growing evidences have been collected over the years to support the concept of dose escalation/dose redistribution using a planned heterogeneous dose prescription, the so-called "dose painting" approach. Validation trials are ongoing, and in the coming years, one may expect to position the dose painting approach in the armamentarium for the treatment of patients with head-and-neck squamous cell carcinoma.
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15
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Garibaldi C, Ronchi S, Cremonesi M, Gilardi L, Travaini L, Ferrari M, Alterio D, Kaanders JH, Ciardo D, Orecchia R, Jereczek-Fossa BA, Grana CM. Interim 18 F-FDG PET/CT During Chemoradiation Therapy in the Management of Head and Neck Cancer Patients: A Systematic Review. Int J Radiat Oncol Biol Phys 2017; 98:555-573. [DOI: 10.1016/j.ijrobp.2017.02.217] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 02/20/2017] [Accepted: 02/23/2017] [Indexed: 01/27/2023]
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16
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Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep 2017; 7:358. [PMID: 28336974 PMCID: PMC5428425 DOI: 10.1038/s41598-017-00426-y] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 02/23/2017] [Indexed: 12/21/2022] Open
Abstract
Imaging with positron emission tomography (PET)/computed tomography (CT) is crucial in the management of cancer because of its value in tumor staging, response assessment, restaging, prognosis and treatment responsiveness prediction. In the last years, interest has grown in texture analysis which provides an "in-vivo" lesion characterization, and predictive information in several malignances including NSCLC; however several drawbacks and limitations affect these studies, especially because of lack of standardization in features calculation, definitions and methodology reporting. The present paper provides a comprehensive review of literature describing the state-of-the-art of FDG-PET/CT texture analysis in NSCLC, suggesting a proposal for harmonization of methodology.
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Affiliation(s)
- M Sollini
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy.
| | - L Cozzi
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
- Radiotherapy and Radiosurgery Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - L Antunovic
- Nuclear Medicine Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - A Chiti
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
- Nuclear Medicine Unit, Humanitas Clinical and Research Center, via Manzoni, 56-20089, Rozzano, (Milan), Italy
| | - M Kirienko
- Department of Biomedical Sciences, Humanitas University, via Manzoni, 113-20089, Rozzano, (Milan), Italy
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17
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Differding S, Sterpin E, Hermand N, Vanstraelen B, Nuyts S, de Patoul N, Denis JM, Lee JA, Grégoire V. Radiation dose escalation based on FDG-PET driven dose painting by numbers in oropharyngeal squamous cell carcinoma: a dosimetric comparison between TomoTherapy-HA and RapidArc. Radiat Oncol 2017; 12:59. [PMID: 28335778 PMCID: PMC5364636 DOI: 10.1186/s13014-017-0793-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 03/01/2017] [Indexed: 12/31/2022] Open
Abstract
Purpose Validation of dose escalation through FDG-PET dose painting (DP) for oropharyngeal squamous cell carcinoma (SCC) requires randomized clinical trials with large sample size, potentially involving different treatment planning and delivery systems. As a first step of a joint clinical study of DP, a planning comparison was performed between Tomotherapy HiArt® (HT) and Varian RapidArc® (RA). Methods The planning study was conducted on five patients with oropharyngeal SCC. Elective and therapeutic CTVs were delineated based on anatomic information, and the respective PTVs (CTVs + 4 mm) were prescribed a dose of 56 (PTV56) and 70 Gy (PTV70). A gradient-based method was used to delineate automatically the external contours of the FDG-PET volume (GTVPET). Variation of the FDG uptake within the GTVPET was linearly converted into a prescription between 70 and 86 Gy. A dilation of the voxel-by-voxel prescription of 2.5 mm was applied to account for geometric errors in dose delivery (PTVPET). The study was divided in two planning phases aiming at maximizing target coverage (phase I) and lowering doses to OAR (phase II). A Quality-Volume Histogram (QVH) assessed conformity with the DP prescription inside the PTVPET. Results In phase I, for both HT and RA, all plans achieved comparable target coverage for PTV56 and PTV70, respecting the planning objectives. A median value of 99.9 and 97.2% of all voxels in the PTVPET received at least 95% of the prescribed dose for RA and HT, respectively. A median value of 0.0% and 3.7% of the voxels in the PTVPET received 105% or more of prescribed dose for RA and HT, respectively. In phase II, no significant differences were found in OAR sparing. Median treatment times were 13.7 min for HT and 5 min for RA. Conclusions Both HT and RA can generate similar dose distributions for FDG-PET based dose escalation and dose painting in oropharyngeal SCC patients. Electronic supplementary material The online version of this article (doi:10.1186/s13014-017-0793-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sarah Differding
- Department of Radiation Oncology, and Center for Molecular Imaging, Oncology and Radiotherapy (MIRO), Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), Brussels, Belgium
| | - Edmond Sterpin
- Department of Radiation Oncology, and Center for Molecular Imaging, Oncology and Radiotherapy (MIRO), Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), Brussels, Belgium
| | - Nicolas Hermand
- Department of Oncology, Experimental Radiation Oncology, KU Leuven - University of Leuven, Leuven, Belgium
| | - Bianca Vanstraelen
- Department of Oncology, Experimental Radiation Oncology, KU Leuven - University of Leuven, Leuven, Belgium
| | - Sandra Nuyts
- Department of Oncology, Experimental Radiation Oncology, KU Leuven - University of Leuven, Leuven, Belgium.,Department of Radiation Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | - Nathalie de Patoul
- Department of Radiation Oncology, St-Luc University Hospital, Avenue Hippocrate 10, B-1200, Bruxelles, Belgium
| | - Jean-Marc Denis
- Department of Radiation Oncology, St-Luc University Hospital, Avenue Hippocrate 10, B-1200, Bruxelles, Belgium
| | - John Aldo Lee
- Department of Radiation Oncology, and Center for Molecular Imaging, Oncology and Radiotherapy (MIRO), Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), Brussels, Belgium
| | - Vincent Grégoire
- Department of Radiation Oncology, and Center for Molecular Imaging, Oncology and Radiotherapy (MIRO), Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), Brussels, Belgium. .,Department of Radiation Oncology, St-Luc University Hospital, Avenue Hippocrate 10, B-1200, Bruxelles, Belgium.
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18
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Lambin P, Zindler J, Vanneste BGL, De Voorde LV, Eekers D, Compter I, Panth KM, Peerlings J, Larue RTHM, Deist TM, Jochems A, Lustberg T, van Soest J, de Jong EEC, Even AJG, Reymen B, Rekers N, van Gisbergen M, Roelofs E, Carvalho S, Leijenaar RTH, Zegers CML, Jacobs M, van Timmeren J, Brouwers P, Lal JA, Dubois L, Yaromina A, Van Limbergen EJ, Berbee M, van Elmpt W, Oberije C, Ramaekers B, Dekker A, Boersma LJ, Hoebers F, Smits KM, Berlanga AJ, Walsh S. Decision support systems for personalized and participative radiation oncology. Adv Drug Deliv Rev 2017; 109:131-153. [PMID: 26774327 DOI: 10.1016/j.addr.2016.01.006] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 12/08/2015] [Accepted: 01/06/2016] [Indexed: 12/12/2022]
Abstract
A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.
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Affiliation(s)
- Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Jaap Zindler
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ben G L Vanneste
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lien Van De Voorde
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daniëlle Eekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Inge Compter
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kranthi Marella Panth
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jurgen Peerlings
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ruben T H M Larue
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Timo M Deist
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evelyn E C de Jong
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Aniek J G Even
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nicolle Rekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marike van Gisbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Erik Roelofs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sara Carvalho
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Catharina M L Zegers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maria Jacobs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Janita van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Patricia Brouwers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jonathan A Lal
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ludwig Dubois
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ala Yaromina
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evert Jan Van Limbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maaike Berbee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bram Ramaekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Liesbeth J Boersma
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kim M Smits
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Adriana J Berlanga
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sean Walsh
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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19
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Even AJG, De Ruysscher D, van Elmpt W. The promise of multiparametric imaging in oncology: how do we move forward? Eur J Nucl Med Mol Imaging 2016; 43:1195-8. [PMID: 27020581 DOI: 10.1007/s00259-016-3361-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 03/01/2016] [Indexed: 01/30/2023]
Affiliation(s)
- Aniek J G Even
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Dr. Tanslaan 12, NL-6229 ET, Maastricht, The Netherlands
| | - Dirk De Ruysscher
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Dr. Tanslaan 12, NL-6229 ET, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Dr. Tanslaan 12, NL-6229 ET, Maastricht, The Netherlands.
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20
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Dahiya K, Dhankhar R. Updated overview of current biomarkers in head and neck carcinoma. World J Methodol 2016; 6:77-86. [PMID: 27018324 PMCID: PMC4804254 DOI: 10.5662/wjm.v6.i1.77] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 11/25/2015] [Accepted: 03/09/2016] [Indexed: 02/06/2023] Open
Abstract
Squamous cell cancer is the most common type of malignancy arising from the epithelial cells of the head and neck region. Head and neck squamous cell carcinoma (HNSCC) is one of the predominant causes of cancer related casualties worldwide. Overall prognosis in this disease has improved to some extent with the advancements in therapeutic modalities but detection of primary tumor at its initial stage and prevention of relapse are the major targets to be achieved for further improvement in terms of survival rate of patients. Latest achievements in basic research regarding molecular characterization of the disease has helped in better perception of the molecular mechanisms involved in HNSCC progression and also in recognizing and targeting various molecular biomarkers associated with HNSCC. In the present article, we review the information regarding latest and potential biomarkers for the early detection of HNSCC. A detailed molecular characterization, ultimately, is likely to improve the development of new therapeutic strategies, potentially relevant to diagnosis and prognosis of head and neck cancers. The need for more accurate and timely disease prediction has generated enormous research interests in this field.
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21
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Multiparametric imaging of patient and tumour heterogeneity in non-small-cell lung cancer: quantification of tumour hypoxia, metabolism and perfusion. Eur J Nucl Med Mol Imaging 2015; 43:240-248. [PMID: 26338178 PMCID: PMC4700090 DOI: 10.1007/s00259-015-3169-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 08/06/2015] [Indexed: 02/07/2023]
Abstract
Purpose Multiple imaging techniques are nowadays available for clinical in-vivo visualization of tumour biology. FDG PET/CT identifies increased tumour metabolism, hypoxia PET visualizes tumour oxygenation and dynamic contrast-enhanced (DCE) CT characterizes vasculature and morphology. We explored the relationships among these biological features in patients with non-small-cell lung cancer (NSCLC) at both the patient level and the tumour subvolume level. Methods A group of 14 NSCLC patients from two ongoing clinical trials (NCT01024829 and NCT01210378) were scanned using FDG PET/CT, HX4 PET/CT and DCE CT prior to chemoradiotherapy. Standardized uptake values (SUV) in the primary tumour were calculated for the FDG and hypoxia HX4 PET/CT scans. For hypoxia imaging, the hypoxic volume, fraction and tumour-to-blood ratio (TBR) were also defined. Blood flow and blood volume were obtained from DCE CT imaging. A tumour subvolume analysis was used to quantify the spatial overlap between subvolumes. Results At the patient level, negative correlations were observed between blood flow and the hypoxia parameters (TBR >1.2): hypoxic volume (−0.65, p = 0.014), hypoxic fraction (−0.60, p = 0.025) and TBR (−0.56, p = 0.042). At the tumour subvolume level, hypoxic and metabolically active subvolumes showed an overlap of 53 ± 36 %. Overlap between hypoxic sub-volumes and those with high blood flow and blood volume was smaller: 15 ± 17 % and 28 ± 28 %, respectively. Half of the patients showed a spatial mismatch (overlap <5 %) between increased blood flow and hypoxia. Conclusion The biological imaging features defined in NSCLC tumours showed large interpatient and intratumour variability. There was overlap between hypoxic and metabolically active subvolumes in the majority of tumours, there was spatial mismatch between regions with high blood flow and those with increased hypoxia. Electronic supplementary material The online version of this article (doi:10.1007/s00259-015-3169-4) contains supplementary material, which is available to authorized users.
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Alonzi R. Functional Radiotherapy Targeting using Focused Dose Escalation. Clin Oncol (R Coll Radiol) 2015; 27:601-17. [PMID: 26456478 DOI: 10.1016/j.clon.2015.06.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 06/17/2015] [Indexed: 12/12/2022]
Abstract
Various quantitative and semi-quantitative imaging biomarkers have been identified that may serve as valid surrogates for the risk of recurrence after radiotherapy. Tumour characteristics, such as hypoxia, vascularity, cellular proliferation and clonogen density, can be geographically mapped using biological imaging techniques. The potential gains in therapeutic ratio from the precision targeting of areas of intrinsic resistance makes focused dose escalation an exciting field of study. This overview will explore the issues surrounding biologically optimised radiotherapy, including its requirements, feasibility, technical considerations and potential applicability.
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Affiliation(s)
- R Alonzi
- Mount Vernon Cancer Centre, Northwood, UK.
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Ree AH, Redalen KR. Personalized radiotherapy: concepts, biomarkers and trial design. Br J Radiol 2015; 88:20150009. [PMID: 25989697 DOI: 10.1259/bjr.20150009] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
In the past decade, and pointing onwards to the immediate future, clinical radiotherapy has undergone considerable developments, essentially including technological advances to sculpt radiation delivery, the demonstration of the benefit of adding concomitant cytotoxic agents to radiotherapy for a range of tumour types and, intriguingly, the increasing integration of targeted therapeutics for biological optimization of radiation effects. Recent molecular and imaging insights into radiobiology will provide a unique opportunity for rational patient treatment, enabling the parallel design of next-generation trials that formally examine the therapeutic outcome of adding targeted drugs to radiation, together with the critically important assessment of radiation volume and dose-limiting treatment toxicities. In considering the use of systemic agents with presumed radiosensitizing activity, this may also include the identification of molecular, metabolic and imaging markers of treatment response and tolerability, and will need particular attention on patient eligibility. In addition to providing an overview of clinical biomarker studies relevant for personalized radiotherapy, this communication will highlight principles in addressing clinical evaluation of combined-modality-targeted therapeutics and radiation. The increasing number of translational studies that bridge large-scale omics sciences with quality-assured phenomics end points-given the imperative development of open-source data repositories to allow investigators the access to the complex data sets-will enable radiation oncology to continue to position itself with the highest level of evidence within existing clinical practice.
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Affiliation(s)
- A H Ree
- 1 Department of Oncology, Akershus University Hospital, Lørenskog, Norway.,2 Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - K R Redalen
- 1 Department of Oncology, Akershus University Hospital, Lørenskog, Norway
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Wang SJ. Surveillance radiologic imaging after treatment of oropharyngeal cancer: a review. World J Surg Oncol 2015; 13:94. [PMID: 25889162 PMCID: PMC4358873 DOI: 10.1186/s12957-015-0481-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 01/22/2015] [Indexed: 01/31/2023] Open
Abstract
The increasing proportion of human papilloma virus-related oropharynx cancers has led to improved success in the treatment of this disease. However, the current low recurrence rate after treatment of oropharyngeal cancer highlights the continued need for, as well as the challenges of, designing an effective follow-up surveillance program. There are frequently multiple modalities used in the treatment of oropharyngeal cancer, resulting in short- and long-term tissue changes to the head and neck that challenge clinical distinction of recurrence versus treatment-related changes. The oropharynx subsite is characterized by complex anatomy not always accessible to physical exam, making radiologic imaging a potentially useful supplement for effective follow-up assessment. In this manuscript, the literature regarding the type of radiologic imaging modality and the frequency of obtaining imaging studies in the surveillance follow-up after treatment of oropharyngeal cancer is reviewed. While ultrasound and MRI have useful characteristics that deserve further study, PET/CT appears to have the best sensitivity and specificity for imaging surveillance follow-up of head and neck cancers including oropharyngeal cancer. A negative PET/CT is particularly useful as a predictor of prognosis and can guide the clinician as to when to stop obtaining additional imaging studies in the absence of clinical signs of recurrence. However, there is scant evidence that imaging surveillance can improve survival outcomes. Suggestions to guide future imaging surveillance research studies are provided.
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Affiliation(s)
- Steven J Wang
- Department of Otolaryngology-Head and Neck Surgery, University of California, 2233 Post St, 3rd Floor, San Francisco, CA, 94115, USA.
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