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Yuan L, An L, Zhu Y, Duan C, Kong W, Jiang P, Yu QQ. Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT. Cancer Manag Res 2024; 16:361-375. [PMID: 38699652 PMCID: PMC11063459 DOI: 10.2147/cmar.s451871] [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: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
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Affiliation(s)
- Lili Yuan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Lin An
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Yandong Zhu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Chongling Duan
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Weixiang Kong
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
| | - Qing-Qing Yu
- Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China
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2
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Ran P, Tan T, Li J, Yang H, Li J, Zhang J. Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning. BMC Cancer 2024; 24:264. [PMID: 38402382 PMCID: PMC10894477 DOI: 10.1186/s12885-024-11930-6] [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: 09/26/2023] [Accepted: 01/29/2024] [Indexed: 02/26/2024] Open
Abstract
AIM Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. METHODS Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. RESULTS Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). CONCLUSION For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.
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Affiliation(s)
- Pan Ran
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Tao Tan
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jinjin Li
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Hao Yang
- Department of Internal Medicine, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Juan Li
- Department of Pharmacy, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Jun Zhang
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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Cui S, Traverso A, Niraula D, Zou J, Luo Y, Owen D, El Naqa I, Wei L. Interpretable artificial intelligence in radiology and radiation oncology. Br J Radiol 2023; 96:20230142. [PMID: 37493248 PMCID: PMC10546466 DOI: 10.1259/bjr.20230142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023] Open
Abstract
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - Alberto Traverso
- Department of Radiotherapy, Maastro Clinic, Maastricht, Netherlands
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Jiaren Zou
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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4
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Ohno Y, Yui M, Yamamoto K, Takenaka D, Koyama H, Nagata H, Ueda T, Ikeda H, Ozawa Y, Toyama H, Yoshikawa T. Chemical Exchange Saturation Transfer MRI: Capability for Predicting Therapeutic Effect of Chemoradiotherapy on Non-Small Cell Lung Cancer Patients. J Magn Reson Imaging 2023; 58:174-186. [PMID: 36971493 DOI: 10.1002/jmri.28691] [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: 01/26/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Amide proton transfer (APT) weighted chemical exchange saturation transfer CEST (APTw/CEST) magnetic resonance imaging (MRI) has been suggested as having the potential for assessing the therapeutic effect of brain tumors or rectal cancer. Moreover, diffusion-weighted imaging (DWI) and positron emission tomography fused with computed tomography by means of 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (FDG-PET/CT) have been suggested as useful in same setting. PURPOSE To compare the capability of APTw/CEST imaging, DWI, and FDG-PET/CT for predicting therapeutic effect of chemoradiotherapy (CRT) on stage III non-small cell lung cancer (NSCLC) patients. STUDY TYPE Prospective. POPULATION Eighty-four consecutive patients with Stage III NSCLC, 45 men (age range, 62-75 years; mean age, 71 years) and 39 women (age range, 57-75 years; mean age, 70 years). All patients were then divided into two groups (Response Evaluation Criteria in Solid Tumors [RECIST] responders, consisting of the complete response and partial response groups, and RECIST non-responders, consisting of the stable disease and progressive disease groups). FIELD STRENGTH/SEQUENCE 3 T, echo planar imaging or fast advanced spin-echo (FASE) sequences for DWI and 2D half Fourier FASE sequences with magnetization transfer pulses for CEST imaging. ASSESSMENT Magnetization transfer ratio asymmetry (MTRasym ) at 3.5 ppm, apparent diffusion coefficient (ADC), and maximum standard uptake value (SUVmax, ) on PET/CT were assessed by means of region of interest (ROI) measurements at primary tumor. STATISTICAL TESTS Kaplan-Meier method followed by log-rank test and Cox proportional hazards regression analysis with multivariate analysis. A P value <0.05 was considered statistically significant. RESULTS Progression-free survival (PFS) and overall survival (OS) had significant difference between two groups. MTRasym at 3.5 ppm (hazard ratio [HR] = 0.70) and SUVmax (HR = 1.41) were identified as significant predictors for PFS. Tumor staging (HR = 0.57) was also significant predictors for OS. DATA CONCLUSION APTw/CEST imaging showed potential performance as DWI and FDG-PET/CT for predicting the therapeutic effect of CRT on stage III NSCLC patients. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Masao Yui
- Canon Medical Systems Corporation, Otawara, Japan
| | | | - Daisuke Takenaka
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
- Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan
| | - Hisanobu Koyama
- Department of Radiology, Osaka Police Hospital, Osaka, Japan
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takahiro Ueda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Hirotaka Ikeda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Yoshiyuki Ozawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takeshi Yoshikawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
- Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan
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Yao X, Li N, Lu R, Wang X, Zhang Y, Wang S. Development of a nomogram for predicting nosocomial infections among patients after cardiac valve replacement surgery. J Clin Nurs 2023; 32:1466-1475. [PMID: 35988041 DOI: 10.1111/jocn.16489] [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: 05/13/2022] [Revised: 07/14/2022] [Accepted: 07/28/2022] [Indexed: 11/30/2022]
Abstract
AIMS AND OBJECTIVES To construct a predictive nomogram of the risk of nosocomial infections among patients after cardiac valve replacement surgery. BACKGROUND Nosocomial infections are a standout challenge that worsens the prognosis of patients after valve replacement surgery. However, studies on the nomogram of nosocomial infections in these patients have remained scarce. DESIGN A retrospective cohort study. METHODS Patients (n = 720) following valve replacement surgery from 2018 to 2019 were selected. LASSO regression and multivariate logistic regression were utilised to ascertain predictors of nosocomial infections. The predictive performance of the nomogram was appraised by calibration and discrimination. Decision and impact curves were used to assess the clinical utility. Internal validation was implemented via 1000 bootstrap samples to mitigate overfitting. TRIPOD guidelines were used in this study. RESULTS One hundred and fifty one patients (20.97%) experienced nosocomial infections following valve replacement surgery. Heart failure, preoperative anaemia, valve material, American Society of Anesthesiologists score ≥ IV, prolonged duration of surgery, duration of mechanical ventilation ≥ 24 h and indwelling nasogastric tube were predictors of nosocomial infections. Using these variables, we developed a predictive nomogram of the occurrence of nosocomial infections and the internal validation results demonstrated good discrimination and calibration of the nomogram. The clinical decision and impact curve revealed significant clinical utility. CONCLUSIONS The present study constructed a nomogram for predicting the risk of nosocomial infections in patients following cardiac valve replacement surgery. This nomogram may strengthen the effective screening of patients at high risk of nosocomial infections. RELEVANCE TO CLINICAL PRACTICE This risk warning tool can assist clinical staff in making decisions and providing individualised infection control measures for patients, which has a significant reference value for clinical practice. NO PATIENT OR PUBLIC CONTRIBUTION The data for this study were obtained from the hospital database, and the entire process of the study did not involve patient participation.
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Affiliation(s)
- Xue Yao
- School of Nursing and Rehabilitation, Shandong University, Jinan, China.,Department of Infection Prevention and Control, Qilu Hospital of Shandong University, Jinan, China
| | - Na Li
- School of Nursing and Rehabilitation, Shandong University, Jinan, China.,Department of Infection Prevention and Control, Qilu Hospital of Shandong University, Jinan, China
| | - Ranran Lu
- School of Nursing and Rehabilitation, Shandong University, Jinan, China.,Department of Infection Prevention and Control, Qilu Hospital of Shandong University, Jinan, China
| | - Xujing Wang
- School of Nursing and Rehabilitation, Shandong University, Jinan, China.,Department of Infection Prevention and Control, Qilu Hospital of Shandong University, Jinan, China
| | - Yujun Zhang
- School of Nursing and Rehabilitation, Shandong University, Jinan, China.,Department of Infection Prevention and Control, Qilu Hospital of Shandong University, Jinan, China
| | - Shuhui Wang
- Department of Infection Prevention and Control, Qilu Hospital of Shandong University, Jinan, China
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Chen K, Hou L, Chen M, Li S, Shi Y, Raynor WY, Yang H. Predicting the Efficacy of SBRT for Lung Cancer with 18F-FDG PET/CT Radiogenomics. Life (Basel) 2023; 13:life13040884. [PMID: 37109413 PMCID: PMC10142286 DOI: 10.3390/life13040884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/18/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Purpose: to develop a radiogenomic model on the basis of 18F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment. Methods: A total of 123 patients with lung cancer who had undergone 18F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients’ PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model. Results: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849–0.851). Conclusions: The radiogenomic model based on 18F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.
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Affiliation(s)
- Kuifei Chen
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Liqiao Hou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Meng Chen
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Shuling Li
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Yangyang Shi
- Department of Radiation Oncology, University of Arizona, Tucson, AZ 85724, USA
| | - William Y. Raynor
- Department of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Haihua Yang
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
- Correspondence: or
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Volpe S, Isaksson LJ, Zaffaroni M, Pepa M, Raimondi S, Botta F, Lo Presti G, Vincini MG, Rampinelli C, Cremonesi M, de Marinis F, Spaggiari L, Gandini S, Guckenberger M, Orecchia R, Jereczek-Fossa BA. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer. Transl Lung Cancer Res 2022; 11:2452-2463. [PMID: 36636424 PMCID: PMC9830263 DOI: 10.21037/tlcr-22-248] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/31/2022] [Indexed: 11/24/2022]
Abstract
Background No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. Methods Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. Results Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). Conclusions Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Giulia Vincini
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Cristiano Rampinelli
- Department of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Filippo de Marinis
- Division of Thoracic Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy;,Division of Thoracic Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Gandini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roberto Orecchia
- Scientific Direction, IEO, European Institute of Oncology IRCCS, Milan, 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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8
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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9
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:1329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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10
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Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review. Clin Oncol (R Coll Radiol) 2021; 34:e107-e122. [PMID: 34763965 DOI: 10.1016/j.clon.2021.10.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/24/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022]
Abstract
Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.
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11
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Oliveira C, Amstutz F, Vuong D, Bogowicz M, Hüllner M, Foerster R, Basler L, Schröder C, Eboulet EI, Pless M, Thierstein S, Peters S, Hillinger S, Tanadini-Lang S, Guckenberger M. Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging. EJNMMI Res 2021; 11:79. [PMID: 34417899 PMCID: PMC8380219 DOI: 10.1186/s13550-021-00809-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/08/2021] [Indexed: 12/25/2022] Open
Abstract
Background Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). Methods A total of 1404 primary tumour radiomic features were extracted from pre-treatment [18F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). Results Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). Conclusions A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-021-00809-3.
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Affiliation(s)
- Carol Oliveira
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. .,Division of Radiation Oncology, Cancer Center of Southeastern Ontario, Queen's University, Kingston, ON, Canada.
| | - Florian Amstutz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Robert Foerster
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lucas Basler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christina Schröder
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Eric I Eboulet
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Sandra Thierstein
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Solange Peters
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Sven Hillinger
- Department of Thoracic Surgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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12
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Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2021; 47:e185-e202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/22/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022] Open
Abstract
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, 33081, Italy
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
| | | | - Martin Vallières
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48103, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Sarah A Mattonen
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
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13
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Jiang YQ, Gao Q, Chen H, Shi XX, Wu JB, Chen Y, Zhang Y, Pang HW, Lin S. Positron Emission Tomography-Based Short-Term Efficacy Evaluation and Prediction in Patients With Non-Small Cell Lung Cancer Treated With Hypo-Fractionated Radiotherapy. Front Oncol 2021; 11:590836. [PMID: 33718144 PMCID: PMC7947869 DOI: 10.3389/fonc.2021.590836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/18/2021] [Indexed: 12/29/2022] Open
Abstract
Background Positron emission tomography is known to provide more accurate estimates than computed tomography when staging non–small cell lung cancer. The aims of this prospective study were to contrast the short-term efficacy of the two imaging methods while evaluating the effects of hypo-fractionated radiotherapy in non-small cell lung cancer, and to establish a short-term efficacy prediction model based on the radiomics features of positron emission tomography. Methods This nonrandomized-controlled trial was conducted from March 2015 to June 2019. Thirty-one lesions of 30 patients underwent the delineation of the regions of interest on positron emission tomography and computed tomography 1 month before, and 3 months after hypo-fractionated radiotherapy. Each patient was evaluated for the differences in local objective response rate between the two images. The Kaplan Meier method was used to analyze the local objective response and subsequent survival duration of the two imaging methods. The 3D Slicer was used to extract the radiomics features based on positron emission tomography. Least absolute shrinkage and selection operator regression was used to eliminate redundant features, and logistic regression analysis was used to develop the curative-effect-predicting model, which was displayed through a radiomics nomogram. Receiver operating characteristic curve and decision curve were used to evaluate the accuracy and clinical usefulness of the prediction model. Results Positron emission tomography-based local objective response rate was significantly higher than that based on computed tomography [70.97% (22/31) and 12.90% (4/31), respectively (p<0.001)]. The mean survival time of responders and non-responders assessed by positron emission tomography was 28.6 months vs. 11.4 months (p=0.29), whereas that assessed by computed tomography was 24.5 months vs. 26 months (p=0.66), respectively. Three radiomics features were screened to establish a personalized prediction nomogram with high area under curve (0.94, 95% CI 0.85–0.99, p<0.001). The decision curve showed a high clinical value of the radiomics nomogram. Conclusions We recommend positron emission tomography for evaluating the short-term efficacy of hypo-fractionated radiotherapy in non-small cell lung cancer, and that the radiomics nomogram could be an important technique for the prediction of short-term efficacy, which might enable an improved and precise treatment. Registration number/URL ChiCTR1900027768/http://www.chictr.org.cn/showprojen.aspx?proj=46057
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Affiliation(s)
- Yi-Qing Jiang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Qin Gao
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Han Chen
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiang-Xiang Shi
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jing-Bo Wu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yue Chen
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yan Zhang
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hao-Wen Pang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Sheng Lin
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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14
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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15
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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16
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Wei L, Cui C, Xu J, Kaza R, El Naqa I, Dewaraja YK. Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics. EJNMMI Phys 2020; 7:74. [PMID: 33296050 PMCID: PMC7726084 DOI: 10.1186/s40658-020-00340-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 11/24/2020] [Indexed: 12/14/2022] Open
Abstract
Purpose To evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90Y PET can be integrated to better predict outcomes in microsphere radioembolization of liver malignancies Methods Given the noisy nature of 90Y PET, first, a liver phantom study with repeated acquisitions and varying reconstruction parameters was used to identify a subset of robust radiomics features for the patient analysis. In 36 radioembolization procedures, 90Y PET/CT was performed within a couple of hours to extract 46 radiomics features and estimate absorbed dose in 105 primary and metastatic liver lesions. Robust radiomics modeling was based on bootstrapped multivariate logistic regression with shrinkage regularization (LASSO) and Cox regression with LASSO. Nested cross-validation and bootstrap resampling were used for optimal parameter/feature selection and for guarding against overfitting risks. Spearman rank correlation was used to analyze feature associations. Area under the receiver-operating characteristics curve (AUC) was used for lesion response (at first follow-up) analysis while Kaplan-Meier plots and c-index were used to assess progression model performance. Models with absorbed dose only, radiomics only, and combined models were developed to predict lesion outcome. Results The phantom study identified 15/46 reproducible and robust radiomics features that were subsequently used in the patient models. A lesion response model with zone percentage (ZP) and mean absorbed dose achieved an AUC of 0.729 (95% CI 0.702–0.758), and a progression model with zone size nonuniformity (ZSN) and absorbed dose achieved a c-index of 0.803 (95% CI 0.790–0.815) on nested cross-validation (CV). Although the combined models outperformed the radiomics only and absorbed dose only models, statistical significance was not achieved with the current limited data set to establish expected superiority. Conclusion We have developed new lesion-level response and progression models using textural radiomics features, derived from 90Y PET combined with mean absorbed dose for predicting outcome in radioembolization. These encouraging, but limited results, will need further validation in independent and larger datasets prior to any clinical adoption. Supplementary Information Supplementary information accompanies this paper at 10.1186/s40658-020-00340-9.
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Affiliation(s)
- Lise Wei
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA
| | - Can Cui
- Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jiarui Xu
- Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ravi Kaza
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Issam El Naqa
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.,Machine Learning Department, Moffitt Cancer Center, Tampa, FL, USA
| | - Yuni K Dewaraja
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
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17
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Abstract
Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.
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Affiliation(s)
- Gary J R Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK.
| | - Vicky Goh
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Radiology Department, Guy's and St Thomas' Hospitals NHS Trust, London, UK
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18
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Kothari G, Korte J, Lehrer EJ, Zaorsky NG, Lazarakis S, Kron T, Hardcastle N, Siva S. A systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy. Radiother Oncol 2020; 155:188-203. [PMID: 33096167 DOI: 10.1016/j.radonc.2020.10.023] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND PURPOSE Radiomics allows extraction of quantifiable features from imaging. This study performs a systematic review and meta-analysis of the performance of radiomics based prognostic models in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS A literature review was performed following PRISMA guidelines. Medline, EMBASE and Cochrane databases were searched for articles investigating radiomics features predictive of overall survival (OS) in NSCLC treated with curative intent radiotherapy. A random-effects meta-analysis of Harrell's Concordance Index (C-index) was performed on the performance of radiomics models. RESULTS Of the 2746 articles retrieved, 40 studies of 55 datasets and 6223 patients were eligible for inclusion in the systematic review. There was significant heterogeneity in the methodology for feature selection and model development. Twelve datasets reported the C-index of radiomics based models in predicting OS and were included in the meta-analysis. The C-index random effects estimate was 0.57 (95% CI 0.53-0.62). There was significant heterogeneity (I2 = 70.3%). CONCLUSIONS Based on this review, radiomics based models for lung cancer have to date demonstrated modest prognostic capabilities. Future research should consider using standardised radiomics features, robust feature selection and model development, and deep learning techniques, absolving the need for pre-defined features, to improve imaging-based models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia.
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Department of Biomedical Engineering, School of Engineering, University of Melbourne, Melbourne, Australia
| | - Eric J Lehrer
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Nicholas G Zaorsky
- Department of Radiation Oncology, Penn State Cancer Institute, Hershey, USA; Department of Public Health Sciences, Penn State College of Medicine, Hershey, USA
| | - Smaro Lazarakis
- Health Sciences Library, Peter MacCallum Cancer Centre, Parkville, Australia
| | - Tomas Kron
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia; Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia
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19
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Validating the SumMean 18F-FDG PET Textural Feature as a Prognostic Marker in an Independent Cohort of Locally Advanced Non-Small Cell Lung Cancer Patients Undergoing Concurrent Chemoradiation Therapy. Pract Radiat Oncol 2020; 11:e46-e51. [PMID: 33091615 DOI: 10.1016/j.prro.2020.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/08/2020] [Accepted: 10/07/2020] [Indexed: 11/23/2022]
Abstract
PURPOSE Analyses from the ACRIN6668/RTOG0235 trial data identified the SumMean textural feature, calculated from 18-fluorodeoxyglucose positron emission tomography for tumors with a metabolic tumor volume >93 cm3, as a predictor of overall survival (OS) for patients with locally advanced non-small cell lung cancer (LA-NSCLC) receiving concurrent chemoradiation therapy. Here, we validated that finding in a completely independent patient cohort from a single institution. METHODS AND MATERIALS We identified patients with LA-NSCLC who underwent staging 18-fluorodeoxyglucose positron emission tomography and received definitive chemoradiation therapy at our institution between 2007 and 2018. Primary tumors were segmented semiautomatically, and SumMean score was calculated for each tumor and categorized according to the previously proposed cutoff of 0.018. In patients with metabolic tumor volume >93 cm3, SumMean was evaluated as a predictor of progression-free survival (PFS) and OS using log rank and Cox proportional hazards testing. RESULTS One hundred forty-eight patients met inclusion criteria, and 34 had large tumors (>93 cm3). Twelve (35%) had high SumMean, and 22 (65%) had low SumMean. SumMean was not significantly associated with other clinical variables. Median PFS for patients with large tumors and low SumMean was 5.8 months, compared with 41.1 months for patients with large tumors and high SumMean (log rank P = .022). Median OS for patients with large tumors and low SumMean was 15.0 months; median OS was not reached for patients with large tumors and high SumMean (log rank P = .014). In multivariable analysis, high SumMean was an independent predictor of improved OS (hazard ratio, 0.26; 95% confidence interval, 0.07-0.94; P = .041) and PFS (hazard ratio, 0.30; 95% confidence interval, 0.10-0.86; P = .026). CONCLUSIONS We externally validated SumMean as a prognostic marker for patients with LA-NSCLC treated with chemoradiation therapy in an independent patient cohort. Future studies will explore potential mechanisms for this association and how textural features may help guide treatment decisions.
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Zhang N, Liang R, Gensheimer MF, Guo M, Zhu H, Yu J, Diehn M, Loo BW, Li R, Wu J. Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. Am J Cancer Res 2020; 10:11707-11718. [PMID: 33052242 PMCID: PMC7546006 DOI: 10.7150/thno.50565] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/08/2020] [Indexed: 12/25/2022] Open
Abstract
Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.
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Gao X, Tham IWK, Yan J. Quantitative accuracy of radiomic features of low-dose 18F-FDG PET imaging. Transl Cancer Res 2020; 9:4646-4655. [PMID: 35117828 PMCID: PMC8797853 DOI: 10.21037/tcr-20-1715] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 07/08/2020] [Indexed: 01/12/2023]
Abstract
Background 18F-FDG PET based radiomics is promising for precision oncology imaging. This work aims to explore quantitative accuracies of radiomic features (RFs) for low-dose 18F-FDG PET imaging. Methods Twenty lung cancer patients were prospectively enrolled and underwent 18F-FDG PET/CT scans. Low-dose PET situations (true counts: 20×106, 15×106, 10×106, 7.5×106, 5×106, 2×106, 1×106, 0.5×106, 0.25×106) were simulated by randomly discarding counts from the acquired list-mode data. Each PET image was created using the scanner default reconstruction parameters. Each lesion volume of interest (VOI) was obtained via an adaptive contouring method with a threshold of 50% peak standardized uptake value (SUVpeak) in the PET images with full count data and VOIs were copied to the PET images at the reduced count level. Conventional SUV measures, features calculated from first-order statistics (FOS) and texture features (TFs) were calculated. Texture based RF include features calculated from gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboring gray-level dependence matrix (NGLDM) and neighbor gray-tone difference matrix (NGTDM). Bias percentage (BP) at different count levels for each RF was calculated. Results Fifty-seven lesions with a volume greater than 1.5 cm3 were found (mean volume, 25.7 cm3, volume range, 1.5–245.4 cm3). In comparison with normal total counts, mean SUV (SUVmean) in the lesions, normal lungs and livers, Entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the most robust features, with a BP of 5% at the count level of 1×106 (equivalent to an effective dose of 0.04 mSv) RF including cluster shade from GLCM, long-run low grey-level emphasis, high grey-level run emphasis and short-run low grey-level emphasis from GLRM exhibited the worst performance with 50% of bias with 20×106 counts (equivalent to an effective dose of 0.8 mSv). Conclusions In terms of the lesions included in this study, SUVmean, entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the least sensitive features to lowering count.
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Affiliation(s)
- Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Ivan W K Tham
- ASTAR-NUS, Clinical Imaging Research Center, Singapore, Singapore.,Department of Radiation Oncology, National University Hospital, Singapore, Singapore.,Department of Radiation Oncology, Mount Elizabeth Novena Hospital, Singapore, Singapore
| | - Jianhua Yan
- Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China.,Molecular Imaging Precision Medicine Collaborative Innovation Center, Shanxi Medical University, Taiyuan, China
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22
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Yang B, Zhong J, Zhong J, Ma L, Li A, Ji H, Zhou C, Duan S, Wang Q, Zhu C, Tian J, Zhang L, Wang F, Zhu H, Lu G. Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer. Front Oncol 2020; 10:1042. [PMID: 32766134 PMCID: PMC7379864 DOI: 10.3389/fonc.2020.01042] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 05/26/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose: In this study, we developed and validated a radiomics nomogram by combining the radiomic features extracted from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images and clinicopathological factors to evaluate the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). Patients and Methods: A total of 315 consecutive patients with NSCLC (221 in the training cohort and 94 in the validation cohort) were enrolled in this study. A total of 840 radiomic features were extracted from the CT and PET images. Three radiomic scores (rad-scores) were calculated using the least absolute shrinkage and selection operator (LASSO) Cox regression based on subsets of CT, PET, and PET/CT radiomic features. A multivariate Cox regression analysis was performed for each rad-score combined with clinicopathological factors to determine the independent risk factors. The OS nomogram was constructed based on the PET/CT rad-score and independent clinicopathological factors. Validation and calibration were conducted to evaluate the performance of the model in the training and validation cohorts, respectively. Results: A total of 144 (45.71%) women and 171 (54.29%) men with NSCLC were enrolled in this study. The PET/CT rad-score combined with the clinical model had the best C-index (0.776 and 0.789 for the training and validation cohorts, respectively). Distant metastasis, stage, carcinoembryonic antigen (CEA), and targeted therapy were independent risk factors for patients with NSCLC. The validation curve showed that the OS nomogram had a strong predictive power in patients' survival. The calibration curve showed that the predicted survival time was significantly close to the observed one. Conclusion: A radiomic nomogram based on 18F-FDG PET/CT rad-score and clinicopathological factors had good predictive performance for the survival outcome, offering feasible, and practical guidance for individualized management of patients with NSCLC.
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Affiliation(s)
- Bin Yang
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jian Zhong
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jing Zhong
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lu Ma
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Ang Li
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hengshan Ji
- Department of Nuclear Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Changsheng Zhou
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | | | - Qinggen Wang
- Department of Medical Imaging, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Chaohui Zhu
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Beijing, China
| | - Jiahe Tian
- Department of Nuclear Medicine, The Chinese PLA General Hospital, Beijing, China
| | - Longjiang Zhang
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Feng Wang
- Department of Nuclear Medicine, First People's Hospital of Nanjing, Nanjing, China
| | - Hong Zhu
- Department of Nuclear Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guangming Lu
- Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features. Nucl Med Commun 2020; 41:560-566. [DOI: 10.1097/mnm.0000000000001193] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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24
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2-[ 18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease. Methods 2020; 188:84-97. [PMID: 32497604 DOI: 10.1016/j.ymeth.2020.05.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022] Open
Abstract
Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
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Brodin NP, Tomé WA, Abraham T, Ohri N. 18F-Fluorodeoxyglucose PET in Locally Advanced Non-small Cell Lung Cancer: From Predicting Outcomes to Guiding Therapy. PET Clin 2020; 15:55-63. [PMID: 31735302 DOI: 10.1016/j.cpet.2019.08.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PET using 18-fluorodeoxyglucose (FDG) has become an important part of the work-up for non-small cell lung cancer (NSCLC). This article summarizes advancements in using FDG-PET for patients with locally advanced NSCLC treated with definitive radiation therapy (RT). This article discusses prognostication of outcome based on pretreatment or midtreatment PET metrics, using textural image features to predict treatment outcomes, and using PET to define RT target volumes and inform RT dose modifications. The role of PET is evolving and is moving toward using quantitative image information, with the overarching goal of individualizing therapy to improve outcomes for patients with NSCLC.
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Affiliation(s)
- N Patrik Brodin
- Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10461, USA.
| | - Wolfgang A Tomé
- Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10461, USA; Department of Neurology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Tony Abraham
- Department of Radiology (Nuclear Medicine), Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Nitin Ohri
- Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10461, USA
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26
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Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020; 196:879-887. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023]
Abstract
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy.
| | | | - Giovanni Pirrone
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
| | - Giovanna Sartor
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
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27
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Konert T, Everitt S, La Fontaine MD, van de Kamer JB, MacManus MP, Vogel WV, Callahan J, Sonke JJ. Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any? PLoS One 2020; 15:e0228793. [PMID: 32097418 PMCID: PMC7041813 DOI: 10.1371/journal.pone.0228793] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 01/22/2020] [Indexed: 12/14/2022] Open
Abstract
In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLMall). The fitted model performance was externally validated (n = 40). The performance of GLMall (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLMall compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLMall led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients.
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Affiliation(s)
- Tom Konert
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sarah Everitt
- Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Matthew D. La Fontaine
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jeroen B. van de Kamer
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Michael P. MacManus
- Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Wouter V. Vogel
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jason Callahan
- Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- * E-mail:
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28
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Cook GJR, Goh V. What can artificial intelligence teach us about the molecular mechanisms underlying disease? Eur J Nucl Med Mol Imaging 2019; 46:2715-2721. [PMID: 31190176 PMCID: PMC6879441 DOI: 10.1007/s00259-019-04370-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 05/23/2019] [Indexed: 12/24/2022]
Abstract
While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.
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Affiliation(s)
- Gary J R Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK.
| | - Vicky Goh
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
- Radiology Department, Guy's and St Thomas' Hospitals NHS Trust, London, UK
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Simon AF, Holmes JH, Schwartz ES. Decreasing radiologist burnout through informatics-based solutions. Clin Imaging 2019; 59:167-171. [PMID: 31821974 DOI: 10.1016/j.clinimag.2019.10.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 01/06/2023]
Abstract
Increased performance demands have interacted with suboptimal use of technology and contributed to burnout among radiologists. Although the problem of radiologist burnout has been well documented, there is a gap in the literature in terms of how technology can be better utilized to lessen the problem. Informatics-based modifications to existing technology hold the potential to reduce the amount of time radiologists spend on noninterpretive tasks, decrease interruptions, facilitate connections with colleagues, and improve patient care. Examples of successful modifications to technology are presented and discussed in relation to how they contribute to improving workplace engagement among radiologists.
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Affiliation(s)
- Andrew F Simon
- Department of Psychology, Seton Hall University, South Orange, NJ, United States of America
| | - John H Holmes
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Erin Simon Schwartz
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
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Tang X. The role of artificial intelligence in medical imaging research. BJR Open 2019; 2:20190031. [PMID: 33178962 PMCID: PMC7594889 DOI: 10.1259/bjro.20190031] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/01/2019] [Accepted: 11/13/2019] [Indexed: 12/22/2022] Open
Abstract
Without doubt, artificial intelligence (AI) is the most discussed topic today in medical imaging research, both in diagnostic and therapeutic. For diagnostic imaging alone, the number of publications on AI has increased from about 100-150 per year in 2007-2008 to 1000-1100 per year in 2017-2018. Researchers have applied AI to automatically recognizing complex patterns in imaging data and providing quantitative assessments of radiographic characteristics. In radiation oncology, AI has been applied on different image modalities that are used at different stages of the treatment. i.e. tumor delineation and treatment assessment. Radiomics, the extraction of a large number of image features from radiation images with a high-throughput approach, is one of the most popular research topics today in medical imaging research. AI is the essential boosting power of processing massive number of medical images and therefore uncovers disease characteristics that fail to be appreciated by the naked eyes. The objectives of this paper are to review the history of AI in medical imaging research, the current role, the challenges need to be resolved before AI can be adopted widely in the clinic, and the potential future.
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31
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Hotta M, Minamimoto R, Miwa K. 11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier. Sci Rep 2019; 9:15666. [PMID: 31666650 PMCID: PMC6821731 DOI: 10.1038/s41598-019-52279-2] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 10/15/2019] [Indexed: 12/22/2022] Open
Abstract
Differentiating recurrent brain tumor from radiation necrosis is often difficult. This study aims to investigate the efficacy of 11C-methionine (MET)-PET radiomics for distinguishing recurrent brain tumor from radiation necrosis, as compared with conventional tumor-to-normal cortex (T/N) ratio evaluation. We enrolled 41 patients with metastatic brain tumor or glioma treated using radiation therapy who underwent MET-PET. The area with a standardized uptake value > 1.3 times that of the normal brain cortex was contoured. Forty-two PET features were extracted and used in a random forest classifier and the diagnostic performance was evaluated using a 10-fold cross-validation scheme. Gini index was measured to identify relevant PET parameters for classification. The reference standard was surgical histopathological analysis or more than 6 months of follow-up with MRI. Forty-four lesions were used for the analysis. Thirty-three and 11 lesions were confirmed as recurrent brain tumor and radiation necrosis, respectively. Radiomics and T/N ratio evaluation showed sensitivities of 90.1% and 60.6%, and specificities of 93.9% and 72.7% with areas under the curve of 0.98 and 0.73, respectively. Gray level co-occurrence matrix dissimilarity was the most pertinent feature for diagnosis. MET-PET radiomics yielded excellent outcome for differentiating recurrent brain tumor from radiation necrosis, which outperformed T/N ratio evaluation.
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Affiliation(s)
- Masatoshi Hotta
- Department of Radiology, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
| | - Ryogo Minamimoto
- Department of Radiology, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Science, International University of Health and Welfare, 2600-1 Kitakanemaru, Ohtawara city, Tochigi, 324-850, Japan
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Schick U, Lucia F, Dissaux G, Visvikis D, Badic B, Masson I, Pradier O, Bourbonne V, Hatt M. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology. Br J Radiol 2019; 92:20190105. [PMID: 31538516 DOI: 10.1259/bjr.20190105] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.
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Affiliation(s)
- Ulrike Schick
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - François Lucia
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Gurvan Dissaux
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Dimitris Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Bogdan Badic
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Department of General and Digestive Surgery, University Hospital, Brest, France
| | - Ingrid Masson
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Olivier Pradier
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Vincent Bourbonne
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
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Lacoppidan T, Vogelius IR, Pøhl M, Strange M, Persson GF, Nygård L. An investigative expansion of a competing risk model for first failure site in locally advanced non-small cell lung cancer. Acta Oncol 2019; 58:1386-1392. [PMID: 31271118 DOI: 10.1080/0284186x.2019.1631475] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Introduction: We hypothesized that gross tumor volume (GTV) of primary tumor (GTVT) and nodal volumes (GTVN) were predictors of first failure site in non-small cell lung cancer (NSCLC). We aimed at also comparing the prognostic model's complexity to its ability to generate absolute risk predictions with emphasis on variables available at the time of diagnosis. Materials and methods: Three hundred and forty-two patients treated with definitive chemoradiotherapy (CRT) for adenocarcinoma (AC) or squamous cell carcinoma (SCC) in 2009-2017 were analyzed. Clinical data, standardized uptake values on FDG-PET/CT, GTVT and GTVN were analyzed using multivariate competing risk models. Results: One hundred and thirty-seven patients had SCC. As first site of failure 49 had locoregional failure (LRF), 40 had distant metastasis (DM) and 24 died with no evidence of disease (NED). In 205 patients with AC, 34 had LRF, 118 had DM as first failure site and 17 died with NED. Performance status predicted LRF (p = .02) and UICC stage risk of DM (p = .05 for stage 3, p < .001 for stage 4). Adding histopathology changed predictions with much reduced risk of LRF in AC compared to SCC (HR = 0.5, 95% CI: (0.3-0.75), p = .001). Conversely, AC had a higher rate of DM than SCC (HR = 2.1, 95% CI: (1.5-3.0], p < .001). Addition of FDG metrics and tumor/nodal volume data predicted DM risk (p = .001), but with smaller impact on absolute risk compared to histopathology. Separation of GTV in nodal and tumor lesions did not improve risk predictions. Conclusions: We quantified the effect of adding volumetric and quantitative imaging to competing risk models of first failure site, but did not find tumor volume components to be important. Histopathology remains the simplest and most important factor in prognosticating failure patterns in NSCLC.
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Affiliation(s)
- Thomas Lacoppidan
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Ivan R. Vogelius
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, Copenhagen University, Denmark
| | - Mette Pøhl
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Malene Strange
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gitte F. Persson
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, Copenhagen University, Denmark
- Department of Oncology, Herlev-Gentofte Hospital, Copenhagen University, Herlev, Denmark
| | - Lotte Nygård
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
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[Study Progress of Radiomics in Precision Medicine for Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2019; 22:385-388. [PMID: 31196373 PMCID: PMC6580079 DOI: 10.3779/j.issn.1009-3419.2019.06.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Precision medicine, imaging first. Radiomics, a method of machine learning in artificial intelligence, provides valuable diagnostic, prognostic or predictive information through quantitative analysis on the tumor's region of interest to support personalized clinical decisions and improve individualized treatment, which could lay a solid foundation for achieving the precision medicine. This review provides a latest advance of the radiomic application of the precision medicine for lung cancer.
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Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019; 104:302-315. [PMID: 30711529 PMCID: PMC6499656 DOI: 10.1016/j.ijrobp.2019.01.087] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 01/17/2019] [Accepted: 01/23/2019] [Indexed: 02/06/2023]
Abstract
Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.
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Affiliation(s)
- Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey.
| | - Hania Al-Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mi Huang
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael V Knopp
- Division of Imaging Science, Department of Radiology, Ohio State University, Columbus, Ohio
| | - Jeff M Michalski
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - James Monroe
- Department of Radiation Oncology, St. Anthony's Cancer Center, St. Louis, Missouri
| | - Ceferino Obcemea
- Radiation Research Program, National Cancer Institute, Bethesda, Maryland
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Timothy Solberg
- Department of Radiation Oncology, University of California-San Francisco, San Francisco, California
| | - Jackie Wu
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Ping Xia
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Ying Xiao
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Issam El Naqa
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
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Shi L, Zhou XL, Sun JJ, Huang JH, Wang X, Li K, Pang PP, Xu YJ, Chen M, Zhang MM. Whole-tumor perfusion CT using texture analysis in unresectable stage IIIA/B non-small cell lung cancer treated with recombinant human endostatin. Quant Imaging Med Surg 2019; 9:968-975. [PMID: 31367551 DOI: 10.21037/qims.2019.06.05] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background To observe the dynamic changes of blood perfusion with whole-tumor computed tomography (CT) perfusion imaging using texture analysis in patients with unresectable stage IIIA/B non-small cell lung cancer (NSCLC) treated with recombinant human endostatin (Endostar). Methods This phase II clinical trial recruited 11 patients diagnosed with stage IIIA/B NSCLC. Histological examination prior to treatment revealed squamous cell carcinoma in 4 cases and adenocarcinoma in 7 cases. All patients underwent contrast-enhanced perfusion CT at baseline and a second CT scan 1 week after treatment initiation with Endostar. CT perfusion images including blood flow (BF), blood volume (BV), and permeability (PMB) were imported into OmniKinetics software to quantitatively assess the texture features. Skewness, kurtosis, and entropy were calculated at baseline and after anti-angiogenic therapy. Changes in tumor were analyzed using Wilcoxon signed-rank test. The association of parameters with survival was evaluated using Cox proportional hazards regression model. Results There were no statistical differences in the mean values of BF, BV, and PMB before and after treatment (P=0.594, 0.477 and 0.328, respectively). The skewness on BF images demonstrated significant differences at baseline and after treatment (0.6±2.7 vs. 1.0±2.6, P=0.010), while skewness of BV and PMB showed no significant variation (P=0.477 and 0.213, respectively). The kurtosis and entropy for BF, BV and PMB showed no significant differences (all P>0.05). In adenocarcinoma, the mean BF showed no significant differences at baseline and after treatment (76.5±25.7 vs. 101.2±46.4, P=0.398), while skewness for BF was significantly higher after treatment than at baseline (-0.19±3.3 vs. 0.59±3.2, P=0.028). No significant associations were found between perfusion CT imaging parameters and progression-free survival. Conclusions These results suggested that blood perfusion showed improvement with whole-tumor perfusion CT using texture analysis in patients with stage IIIA/B NSCLC treated by Endostar.
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Affiliation(s)
- Lei Shi
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China.,Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China.,Hangzhou YITU Healthcare Technology Co., Ltd., Hangzhou 310000, China
| | - Xiang-Lan Zhou
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Jing-Jing Sun
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Jie-Hui Huang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Xu Wang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Kai Li
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China
| | | | - Yu-Jin Xu
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China.,Zhejiang Key Laboratory of Radiation Oncology, Hangzhou 310022, China
| | - Ming Chen
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China.,Zhejiang Key Laboratory of Radiation Oncology, Hangzhou 310022, China
| | - Min-Ming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
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Shen WC, Chen SW, Wu KC, Hsieh TC, Liang JA, Hung YC, Yeh LS, Chang WC, Lin WC, Yen KY, Kao CH. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [ 18F]-fluorodeoxyglucose positron emission tomography/computed tomography. Eur Radiol 2019; 29:6741-6749. [PMID: 31134366 DOI: 10.1007/s00330-019-06265-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 04/18/2019] [Accepted: 05/03/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND We designed a deep learning model for assessing 18F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer. METHODS All 142 patients with cervical cancer underwent 18F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. In each round of k-fold cross-validation, a well-trained proposed model and a slice-based optimal threshold were derived from a training set and used to classify each slice set in the test set into the categories of with or without local or distant failure. The classification results of each tumor were aggregated to summarize a tumor-based prediction result. RESULTS In total, 21 and 26 patients experienced local and distant failures, respectively. Regarding local recurrence, the tumor-based prediction result summarized from all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively. CONCLUSION This is the first study to use deep learning model for assessing 18F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. KEY POINTS • This is the first study to use deep learning model for assessing 18 F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. • All 142 patients with cervical cancer underwent 18 F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. • For local recurrence, all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.
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Affiliation(s)
- Wei-Chih Shen
- Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
| | - Shang-Wen Chen
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.,School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kuo-Chen Wu
- Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
| | - Te-Chun Hsieh
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan.,Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Ji-An Liang
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.,Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, No. 2, Yuh-Der Road, Taichung, 404, Taiwan
| | - Yao-Ching Hung
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Lian-Shung Yeh
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Wei-Chun Chang
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Wu-Chou Lin
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Kuo-Yang Yen
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan.,Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Chia-Hung Kao
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan. .,Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, No. 2, Yuh-Der Road, Taichung, 404, Taiwan. .,Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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Advanced PET imaging in oncology: status and developments with current and future relevance to lung cancer care. Curr Opin Oncol 2019; 30:77-83. [PMID: 29251666 DOI: 10.1097/cco.0000000000000430] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE OF REVIEW This review highlights the status and developments of PET imaging in oncology, with particular emphasis on lung cancer. We discuss the significance of PET for diagnosis, staging, decision-making, monitoring of treatment response, and drug development. The PET key advantage, the noninvasive assessment of functional and molecular tumor characteristics including tumor heterogeneity, as well as PET trends relevant to cancer care are exemplified. RECENT FINDINGS Advances of PET and radiotracer technology are encouraging for multiple fields of oncological research and clinical application, including in-depth assessment of PET images by texture analysis (radiomics). Whole body PET imaging and novel PET tracers allow assessing characteristics of most types of cancer. However, only few PET tracers in addition to F-fluorodeoxyglucose have sufficiently been validated, approved, and are reimbursed for a limited number of indications. Therefore, validation and standardization of PET parameters including tracer dosage, image acquisition, post processing, and reading are required to expand PET imaging as clinically applicable approach. SUMMARY Considering the potential of PET imaging for precision medicine and drug development in lung and other types of cancer, increasing efforts are warranted to standardize PET technology and to provide evidence for PET imaging as a guiding biomarker in nearly all areas of cancer treatment.
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Wang L, Dong T, Xin B, Xu C, Guo M, Zhang H, Feng D, Wang X, Yu J. Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer. Eur Radiol 2019; 29:2958-2967. [PMID: 30643940 DOI: 10.1007/s00330-018-5949-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/07/2018] [Accepted: 12/04/2018] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To determine the integrative value of clinical, hematological, and computed tomography (CT) radiomic features in survival prediction for locally advanced non-small cell lung cancer (LA-NSCLC) patients. METHODS Radiomic features and clinical and hematological features of 118 LA-NSCLC cases were firstly extracted and analyzed in this study. Then, stable and prognostic radiomic features were automatically selected using the consensus clustering method with either Cox proportional hazard (CPH) model or random survival forest (RSF) analysis. Predictive radiomic, clinical, and hematological parameters were subsequently fitted into a final prognostic model using both the CPH model and the RSF model. A multimodality nomogram was then established from the fitting model and was cross-validated. Finally, calibration curves were generated with the predicted versus actual survival status. RESULTS Radiomic features selected by clustering combined with CPH were found to be more predictive, with a C-index of 0.699 in comparison to 0.648 by clustering combined with RSF. Based on multivariate CPH model, our integrative nomogram achieved a C-index of 0.792 and retained 0.743 in the cross-validation analysis, outperforming radiomic, clinical, or hematological model alone. The calibration curve showed agreement between predicted and actual values for the 1-year and 2-year survival prediction. Interestingly, the selected important radiomic features were significantly correlated with levels of platelet, platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR) (p values all < 0.05). CONCLUSIONS The integrative nomogram incorporated CT radiomic, clinical, and hematological features improved survival prediction in LA-NSCLC patients, which would offer a feasible and practical reference for individualized management of these patients. KEY POINTS • An integrative nomogram incorporated CT radiomic, clinical, and hematological features was constructed and cross-validated to predict prognosis of LA-NSCLC patients. • The integrative nomogram outperformed radiomic, clinical, or hematological model alone. • This nomogram has value to permit non-invasive, comprehensive, and dynamical evaluation of the phenotypes of LA-NSCLC and can provide a feasible and practical reference for individualized management of LA-NSCLC patients.
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Affiliation(s)
- Linlin Wang
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China
| | - Taotao Dong
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, Jinan, China
| | - Bowen Xin
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia
| | - Chongrui Xu
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia
| | - Meiying Guo
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China
- Medical College of Shandong University, Jinan, China
| | - Huaqi Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China
- Tianjin Medical University, Tianjin, China
| | - Dagan Feng
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia
| | - Xiuying Wang
- School of Information Technologies, the University of Sydney, Building J12, Sydney, NSW, 2006, Australia.
| | - Jinming Yu
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Science, No. 440, Ji Yan Road, Jinan, 250017, China.
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Aide N, Salomon T, Blanc-Fournier C, Grellard JM, Levy C, Lasnon C. Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics. EJNMMI Res 2018; 8:114. [PMID: 30594961 PMCID: PMC6311169 DOI: 10.1186/s13550-018-0466-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 12/10/2018] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The aim of this study is to determine if the choice of the 18F-FDG-PET protocol, especially matrix size and reconstruction algorithm, is of importance to discriminate between immunohistochemical subtypes (luminal versus non-luminal) in breast cancer with textural features (TFs). PROCEDURES Forty-seven patients referred for breast cancer staging in the framework of a prospective study were reviewed as part of an ancillary study. In addition to standard PET imaging (PSFWholeBody), a high-resolution breast acquisition was performed and reconstructed with OSEM and PSF (OSEMbreast/PSFbreast). PET standard metrics and TFs were extracted. For each reconstruction protocol, a prediction model for tumour classification was built using a random forests method. Spearman coefficients were used to seek correlation between PET metrics. RESULTS PSFWholeBody showed lower numbers of voxels within VOIs than OSEMbreast and PSFbreast with median (interquartile range) equal to 130 (43-271), 316 (167-1042), 367 (107-1221), respectively (p < 0.0001). Therefore, using LifeX software, 28 (59%), 46 (98%) and 42 (89%) patients were exploitable with PSFWholeBody, OSEMbreast and PSFbreast, respectively. On matched comparisons, PSFbreast reconstruction presented better abilities than PSFwholeBody and OSEMbreast for the classification of luminal versus non-luminal breast tumours with an accuracy reaching 85.7% as compared to 67.8% for PSFwholeBody and 73.8% for OSEMbreast. PSFbreast accuracy, sensitivity, specificity, PPV and NPV were equal to 85.7%, 94.3%, 42.9%, 89.2%, 60.0%, respectively. Coarseness and ZLNU were found to be main variables of importance, appearing in all three prediction models. Coarseness was correlated with SUVmax on PSFwholeBody images (ρ = - 0.526, p = 0.005), whereas it was not on OSEMbreast (ρ = - 0.183, p = 0.244) and PSFbreast (ρ = - 0.244, p = 0.119) images. Moreover, the range of its values was higher on PSFbreast images as compared to OSEMbreast, especially in small lesions (MTV < 3 ml). CONCLUSIONS High-resolution breast PET acquisitions, applying both small-voxel matrix and PSF modelling, appeared to improve the characterisation of breast tumours.
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Affiliation(s)
- Nicolas Aide
- Nuclear Medicine Department, University Hospital, Caen, France.,INSERM 1199 ANTICIPE, Normandy University, Caen, France
| | | | | | - Jean-Michel Grellard
- Biostatistics and Clinical Research Unit, François Baclesse Cancer Centre, Caen, France
| | - Christelle Levy
- Breast Cancer Unit, François Baclesse Cancer Centre, Caen, France
| | - Charline Lasnon
- INSERM 1199 ANTICIPE, Normandy University, Caen, France. .,Nuclear Medicine Department, François Baclesse Cancer Centre, 3 Avenue du Général Harris, BP 45026 Cedex 5, 14076, Caen, France.
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Soufi M, Arimura H, Nagami N. Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features. Med Phys 2018; 45:5116-5128. [PMID: 30230556 DOI: 10.1002/mp.13202] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 08/29/2018] [Accepted: 09/10/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To identify the optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based (WDB) radiomic features in CT images. MATERIALS AND METHODS The CT images of patients with histologically confirmed nonsmall cell lung carcinomas (NSCLCs) in training (Dataset T; n = 162) and validation (Dataset V; n = 143) datasets were analyzed for this study. The optimal mother wavelets were identified based on the impacts of the WDB radiomic features on the patient survival times. Four hundred and thirty-two three-dimensional WDB radiomic features were calculated from regions of interest (ROI) of 162 tumor contours. A Coxnet algorithm was used to select a subset of radiomic features (signature) based on the prediction of survival times with a fivefold cross validation. The impacts of the radiomic features on the patients' survival times were assessed by using a multivariate Cox proportional hazard regression (MCPHR) model. The major contribution of this study was to identify optimal mother wavelets based on a maximization of a novel ranking index (RI) incorporating the Coxnet cross-validated partial log-likelihood and the summation of the P-values of the radiomic features in the MCPHR model on Dataset T. The prognostic performance of the optimal mother wavelets was validated based on the concordance index (CI) of the MCPHR models when applied to Dataset V. The proposed approach was tested by using 31 mother wavelets from 6 wavelet families that were available in a commercially available software (Matlab® 2016b). RESULTS The optimal mother wavelets were Symlet 5 and Biorthogonal 2.6 at 128 requantization levels, which yielded RIs of 4.27 ± 0.29 (3 features) and 6.50 ± 0.50 (5 features), respectively. The CIs of the MCPHR models of Symlet 5 were 0.66 ± 0.03 (Dataset T) and 0.64 ± 0.00 (Dataset V), whereas those of Biorthogonal 2.6 were 0.68 ± 0.03 (Dataset T) and 0.62 ± 0.02 (Dataset V). The radiomic signatures included the GLRLM-based LHL gray level nonuniformity feature that demonstrated statistically significant differences in stratifying patients with better and worse prognoses in Datasets T and V. CONCLUSION This study has revealed the potential of Symlet and Biorthogonal mother wavelets in the survival prediction of lung cancer patients by using WDB radiomic features in CT images.
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Affiliation(s)
- Mazen Soufi
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, 630-0192, Japan
| | - Hidetaka Arimura
- Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Noriyuki Nagami
- Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
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42
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Zaidi H, Alavi A, Naqa IE. Novel Quantitative PET Techniques for Clinical Decision Support in Oncology. Semin Nucl Med 2018; 48:548-564. [PMID: 30322481 DOI: 10.1053/j.semnuclmed.2018.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Quantitative image analysis has deep roots in the usage of positron emission tomography (PET) in clinical and research settings to address a wide variety of diseases. It has been extensively employed to assess molecular and physiological biomarkers in vivo in healthy and disease states, in oncology, cardiology, neurology, and psychiatry. Quantitative PET allows relating the time-varying activity concentration in tissues/organs of interest and the basic functional parameters governing the biological processes being studied. Yet, quantitative PET is challenged by a number of degrading physical factors related to the physics of PET imaging, the limitations of the instrumentation used, and the physiological status of the patient. Moreover, there is no consensus on the most reliable and robust image-derived PET metric(s) that can be used with confidence in clinical oncology owing to the discrepancies between the conclusions reported in the literature. There is also increasing interest in the use of artificial intelligence based techniques, particularly machine learning and deep learning techniques in a variety of applications to extract quantitative features (radiomics) from PET including image segmentation and outcome prediction in clinical oncology. These novel techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical molecular imaging community and biomedical researchers at large. In this report, we summarize recent developments and future tendencies in quantitative PET imaging and present example applications in clinical decision support to illustrate its potential in the context of clinical oncology.
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Affiliation(s)
- Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva Neuroscience Centre, University of Geneva, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
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43
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Prognostic Value of Pretreatment FDG-PET Parameters in High-dose Image-guided Radiotherapy for Oligometastatic Non–Small-cell Lung Cancer. Clin Lung Cancer 2018; 19:e581-e588. [DOI: 10.1016/j.cllc.2018.04.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 04/05/2018] [Accepted: 04/12/2018] [Indexed: 12/17/2022]
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44
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Heterogeneity analysis of 18F-FDG PET imaging in oncology: clinical indications and perspectives. Clin Transl Imaging 2018. [DOI: 10.1007/s40336-018-0299-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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45
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Chen SW, Shen WC, Hsieh TC, Liang JA, Hung YC, Yeh LS, Chang WC, Lin WC, Yen KY, Kao CH. Textural features of cervical cancers on FDG-PET/CT associate with survival and local relapse in patients treated with definitive chemoradiotherapy. Sci Rep 2018; 8:11859. [PMID: 30089896 PMCID: PMC6082904 DOI: 10.1038/s41598-018-30336-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 07/23/2018] [Indexed: 01/18/2023] Open
Abstract
We retrospectively reviewed the records of 142 patients with stage IB–IIIB cervical cancer who underwent 18F-FDG-PET/CT before external beam radiotherapy plus intracavitary brachytherapy and concurrent chemotherapy. The patients were divided into training and validation cohorts to confirm the reliability of predictors for recurrence. Kaplan–Meier analysis was performed and a Cox regression model was used to examine the effects of variables on overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), and pelvic relapse-free survival (PRFS). High gray-level run emphasis (HGRE) derived from gray-level run-length matrix most accurately and consistently predicted the presence of pelvic residual or recurrent tumors for both cohorts. In multivariate analysis, stages IIIA–IIIB (P = 0.001, hazard ratio [HR] = 4.07) and a low HGRE (P < 0.0001, HR = 4.34) were prognostic factors for low OS, whereas a low HGRE (P = 0.001, HR = 2.86) and nonsquamous cell histology (P = 0.003, HR = 2.76) were prognostic factors for inferior PFS. The nonsquamous cell histology (P < 0.0001, HR = 9.19) and a low HGRE (P = 0.001, HR = 4.69) were predictors for low PRFS. In cervical cancer patients receiving definitive chemoradiotherapy, pretreatment textural features on 18F-FDG-PET/CT can supplement the prognostic information.
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Affiliation(s)
- Shang-Wen Chen
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.,School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wei-Chih Shen
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.,Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
| | - Te-Chun Hsieh
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan.,Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Ji-An Liang
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.,Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Yao-Ching Hung
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Lian-Shung Yeh
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Wei-Chun Chang
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Wu-Chou Lin
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
| | - Kuo-Yang Yen
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan.,Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Chia-Hung Kao
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan. .,Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan. .,Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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46
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Integrated texture parameter of 18F-FDG PET may be a stratification factor for the survival of nonoperative patients with locally advanced non-small-cell lung cancer. Nucl Med Commun 2018; 39:732-740. [DOI: 10.1097/mnm.0000000000000860] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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47
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Konert T, van de Kamer JB, Sonke JJ, Vogel WV. The developing role of FDG PET imaging for prognostication and radiotherapy target volume delineation in non-small cell lung cancer. J Thorac Dis 2018; 10:S2508-S2521. [PMID: 30206495 DOI: 10.21037/jtd.2018.07.101] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Advancements in functional imaging technology have allowed new possibilities in contouring of target volumes, monitoring therapy, and predicting treatment outcome in non-small cell lung cancer (NSCLC). Consequently, the role of 18F-fluorodeoxyglucose positron emission tomography (FDG PET) has expanded in the last decades from a stand-alone diagnostic tool to a versatile instrument integrated with computed tomography (CT), with a prominent role in lung cancer radiotherapy. This review outlines the most recent literature on developments in FDG PET imaging for prognostication and radiotherapy target volume delineation (TVD) in NSCLC. We also describe the challenges facing the clinical implementation of these developments and present new ideas for future research.
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Affiliation(s)
- Tom Konert
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jeroen B van de Kamer
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Wouter V Vogel
- Nuclear Medicine Department, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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48
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A systematic review of the prognostic value of texture analysis in 18F-FDG PET in lung cancer. Ann Nucl Med 2018; 32:602-610. [DOI: 10.1007/s12149-018-1281-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]
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49
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Lee SW, Park H, Lee HY, Sohn I, Lee SH, Kang J, Sun JM, Ahn MJ. Deciphering Clinicoradiologic Phenotype for Thymidylate Synthase Expression Status in Patients with Advanced Lung Adenocarcinoma Using a Radiomics Approach. Sci Rep 2018; 8:8968. [PMID: 29895834 PMCID: PMC5997691 DOI: 10.1038/s41598-018-27273-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 05/31/2018] [Indexed: 01/08/2023] Open
Abstract
We aimed to identify predictive clinicoradiologic characteristics of thymidylate synthase (TS) expression status in advanced non-squamous non-small cell lung cancer patients. We reviewed clinicoradiologic features of 169 patients stratified into TS-negative (n = 84) and TS-positive (n = 85) groups, including quantitative CT radiomic features of both primary lung and metastatic lesions from initial CT and PET. Clinical factors including age and smoking history were significantly associated with TS as well as radiomic features. The predictive performance for dichotomizing TS expression status was slightly higher when imaging features of primary lung lesions were added compared to the model based solely on the clinical features, but without statistical significance (10-fold cross-validated AUC = 0.619 and 0.581, respectively; P = 0.425). The predictive performance of clinicoradiologic parameters slightly increased with primary lung lesions only compared to the inclusion of metastatic lesions, but without statistical significance (10-fold cross-validated AUC = 0.619 and 0.554, respectively; P = 0.203). Overall survival was prolonged in the TS-negative group compared to the TS-positive group (P = 0.001). TS-negativity is a potential prognostic biomarker, and our study presents that although CT radiomic features have potential for predicting TS expression status, clinical significance is uncertain. The addition of radiomic features to clinical factors did not show significant improvement in predicting TS-negativity.
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Affiliation(s)
- So Won Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Radiology, CHA Gangnam Medical Center, CHA University School of Medicine, Seoul, Korea
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
| | - Insuk Sohn
- Statistics and Data Center, Samsung Medical Center, Seoul, Korea
| | - Seung-Hak Lee
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
| | - Jun Kang
- Department of Pathology, Inchun St. Mary's Hospital, College of Medicine, Catholic University of Korea, Inchun, Korea
| | - Jong-Mu Sun
- Division of Hematology-Oncology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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50
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Luo Y, McShan DL, Matuszak MM, Ray D, Lawrence TS, Jolly S, Kong FM, Ten Haken RK, Naqa IE. A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys 2018; 45:10.1002/mp.13029. [PMID: 29862533 PMCID: PMC6279602 DOI: 10.1002/mp.13029] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 05/28/2018] [Accepted: 05/28/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response-adapted personalized treatment planning. METHODS A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty-eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO-BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross-validation (CV) was used to guide the MO-BN structure learning. Area under the free-response receiver operating characteristic (AU-FROC) curve was used to evaluate joint prediction performance. RESULTS Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter-relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO-BN. The joint LC/RP2 prediction yielded an AU-FROC of 0.80 (95% CI: 0.70-0.86) upon internal CV. This improved to 0.85 (0.75-0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during-treatment MO-BN. This MO-BN model outperformed combined single-objective Bayesian networks (SO-BNs) during-treatment [0.78 (0.67-0.84)]. AU-FROC values in the evaluation of the MO-BN and individual SO-BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during-treatment, respectively. CONCLUSIONS MO-BNs can reveal possible biophysical cross-talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during-treatment information. The developed MO-BNs can be an important component of decision support systems for personalized response-adapted radiotherapy.
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Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Daniel L. McShan
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Martha M. Matuszak
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Dipankar Ray
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Theodore S. Lawrence
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Shruti Jolly
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Feng-Ming Kong
- Department of Radiation Oncology, Indiana University, Indianapolis, Indiana, 46202 United States
| | - Randall K. Ten Haken
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
| | - Issam El Naqa
- Department of Radiation Oncology, the University of Michigan, Ann Arbor, Michigan, 48103 United States
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