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Sun Z, Zhang Y, Xia Y, Ba X, Zheng Q, Liu J, Kuang X, Xie H, Gong P, Shi Y, Mao N, Wang Y, Liu M, Ran C, Wang C, Wang X, Li M, Zhang W, Fang Z, Liu W, Guo H, Ma H, Song Y. Association between CT-based adipose variables, preoperative blood biochemical indicators and pathological T stage of clear cell renal cell carcinoma. Heliyon 2024; 10:e24456. [PMID: 38268833 PMCID: PMC10803934 DOI: 10.1016/j.heliyon.2024.e24456] [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: 10/04/2023] [Revised: 01/02/2024] [Accepted: 01/09/2024] [Indexed: 01/26/2024] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is corelated with tumor-associated material (TAM), coagulation system and adipocyte tissue, but the relationships between them have been inconsistent. Our study aimed to explore the cut-off intervals of variables that are non-linearly related to ccRCC pathological T stage for providing clues to understand these discrepancies, and to effectively preoperative risk stratification. Methods This retrospective analysis included 218 ccRCC patients with a clear pathological T stage between January 1st, 2014, and November 30th, 2021. The patients were categorized into two cohorts based on their pathological T stage: low T stage (T1 and T2) and high T stage (T3 and T4). Abdominal and perirenal fat variables were measured based on preoperative CT images. Blood biochemical indexes from the last time before surgery were also collected. The generalized sum model was used to identify cut-off intervals for nonlinear variables. Results In specific intervals, fibrinogen levels (FIB) (2.63-4.06 g/L) and platelet (PLT) counts (>200.34 × 109/L) were significantly positively correlated with T stage, while PLT counts (<200.34 × 109/L) were significantly negatively correlated with T stage. Additionally, tumor-associated material exhibited varying degrees of positive correlation with T stage at different cut-off intervals (cut-off value: 90.556 U/mL). Conclusion Preoperative PLT, FIB and TAM are nonlinearly related to pathological T stage. This study is the first to provide specific cut-off intervals for preoperative variables that are nonlinearly related to ccRCC T stage. These intervals can aid in the risk stratification of ccRCC patients before surgery, allowing for developing a more personalized treatment planning.
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Affiliation(s)
- Zehua Sun
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yumei Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yuanhao Xia
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
- Department of Radiology, Binzhou Medical University, Yantai, 264000, Shandong, China
| | - Xinru Ba
- Department of Radiology, Yantaishan Hospital, Yantai, 264000, Shandong, China
| | - Qingyin Zheng
- Department of Otolaryngology-Head & Neck Surgery, Case Western Reserve University, Cleveland, OH, 44106, United States
| | - Jing Liu
- Department of Pediatrics, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Xiaojing Kuang
- School of Basic Medicine, Qingdao University, Qingdao, 266021, Shandong, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Peiyou Gong
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yongtao Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Ming Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Chao Ran
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Chenchen Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Xiaoni Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Min Li
- Department of Radiology, Yantai Traditional Chinese Medicine Hospital, Yantai, 264000, Shandong, China
| | - Wei Zhang
- Department of Radiology, Yantai Penglai People's Hospital, Yantai, 265600, Shandong, China
| | - Zishuo Fang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610000, China
| | - Wanchen Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Hao Guo
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yang Song
- Department of Nutrition and Food Hygiene, School of Public Health, College of Medicine, Qingdao University, Qingdao, 266021, Shandong, China
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Dou Y, Li X, Tao J, Dong Y, Xu N, Wang S. Prediction of high-grade soft-tissue sarcoma using a combined intratumoural and peritumoural MRI-based radiomics nomogram. Clin Radiol 2023; 78:e1032-e1040. [PMID: 37748959 DOI: 10.1016/j.crad.2023.08.020] [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: 11/20/2022] [Revised: 08/03/2023] [Accepted: 08/29/2023] [Indexed: 09/27/2023]
Abstract
AIM To develop an intratumoural and peritumoural magnetic resonance imaging (MRI)-based radiomics nomogram for predicting tumour grade to improve clinical treatment and long-term prognosis. MATERIALS AND METHODS MRI (3 T) features and T2-weighted imaging with fat-saturation (T2WI-FS)-based radiomics features of 57 patients with soft-tissue sarcoma (STS) were analysed retrospectively. Tumour size, ratio of width and length, relative depth to the peripheral fascia, peritumoural oedema, heterogeneity on T2WI, necrosis signal, enhancement model, and peritumoural enhancement were obtained. Independent risk factors were screened to construct an MRI feature nomogram. Radiomics features were obtained from intratumoural and peritumoural images on T2WI-FS. The optimal radiomics model was selected by the four-step dimensionality reduction method of minimum and maximum normalisation, optimal feature selection, selection based on support vector machine with L1-norm regularisation model, and iterative feature selection. MRI features and optimal radiomics features were used to construct a radiomics nomogram. The MRI feature nomogram model, the radiomics model, and the radiomics nomogram model were assessed by receiver operating characteristic (ROC) curves and calibration curves of the training and validation sets. RESULTS Heterogeneity on T2WI and peritumoural enhancement were independent risk factors for predicting high-grade STS. The areas under the curves of the training set and verification set of the three models were as follows: MRI feature nomogram, 0.86 and 0.83, respectively; intratumoural and peritumoural combined radiomics model, 0.99 and 0.86, respectively; and radiomics nomogram model, 0.98 and 0.96, respectively. CONCLUSION The radiomics nomogram model based on MRI features and combined intratumoural and peritumoural radiomic features was best able to predict high-grade STS.
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Affiliation(s)
- Y Dou
- Department of Ultrasound, The First Affiliated Hospital, Dalian Medical University, Dalian, China; Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - X Li
- Department of Radiology, Huashan Hospital Fudan University, Shanghai, China
| | - J Tao
- Department of Pathology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Y Dong
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - N Xu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China
| | - S Wang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian, China.
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Pesapane F, De Marco P, Rapino A, Lombardo E, Nicosia L, Tantrige P, Rotili A, Bozzini AC, Penco S, Dominelli V, Trentin C, Ferrari F, Farina M, Meneghetti L, Latronico A, Abbate F, Origgi D, Carrafiello G, Cassano E. How Radiomics Can Improve Breast Cancer Diagnosis and Treatment. J Clin Med 2023; 12:jcm12041372. [PMID: 36835908 PMCID: PMC9963325 DOI: 10.3390/jcm12041372] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Correspondence: ; Tel.: +39-02-574891
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rapino
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Eleonora Lombardo
- UOC of Diagnostic Imaging, Policlinico Tor Vergata University, 00133 Rome, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Priyan Tantrige
- Department of Radiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Chiara Trentin
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Mariagiorgia Farina
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Francesca Abbate
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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Musall BC, Adrada BE, Candelaria RP, Mohamed RMM, Abdelhafez AH, Son JB, Sun J, Santiago L, Whitman GJ, Moseley TW, Scoggins ME, Mahmoud HS, White JB, Hwang KP, Elshafeey NA, Boge M, Zhang S, Litton JK, Valero V, Tripathy D, Thompson AM, Yam C, Wei P, Moulder SL, Pagel MD, Yang WT, Ma J, Rauch GM. Quantitative Apparent Diffusion Coefficients From Peritumoral Regions as Early Predictors of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer. J Magn Reson Imaging 2022; 56:1901-1909. [PMID: 35499264 PMCID: PMC9626398 DOI: 10.1002/jmri.28219] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) is a strong predictor of patient survival. Edema in the peritumoral region (PTR) has been reported to be a negative prognostic factor in TNBC. PURPOSE To determine whether quantitative apparent diffusion coefficient (ADC) features from PTRs on reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) predict the response to NAST in TNBC. STUDY TYPE Prospective. POPULATION/SUBJECTS A total of 108 patients with biopsy-proven TNBC who underwent NAST and definitive surgery during 2015-2020. FIELD STRENGTH/SEQUENCE A 3.0 T/rFOV single-shot diffusion-weighted echo-planar imaging sequence (DWI). ASSESSMENT Three scans were acquired longitudinally (pretreatment, after two cycles of NAST, and after four cycles of NAST). For each scan, 11 ADC histogram features (minimum, maximum, mean, median, standard deviation, kurtosis, skewness and 10th, 25th, 75th, and 90th percentiles) were extracted from tumors and from PTRs of 5 mm, 10 mm, 15 mm, and 20 mm in thickness with inclusion and exclusion of fat-dominant pixels. STATISTICAL TESTS ADC features were tested for prediction of pCR, both individually using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC), and in combination in multivariable models with k-fold cross-validation. A P value < 0.05 was considered statistically significant. RESULTS Fifty-one patients (47%) had pCR. Maximum ADC from PTR, measured after two and four cycles of NAST, was significantly higher in pCR patients (2.8 ± 0.69 vs 3.5 ± 0.94 mm2 /sec). The top-performing feature for prediction of pCR was the maximum ADC from the 5-mm fat-inclusive PTR after cycle 4 of NAST (AUC: 0.74; 95% confidence interval: 0.64, 0.84). Multivariable models of ADC features performed similarly for fat-inclusive and fat-exclusive PTRs, with AUCs ranging from 0.68 to 0.72 for the cycle 2 and cycle 4 scans. DATA CONCLUSION Quantitative ADC features from PTRs may serve as early predictors of the response to NAST in TNBC. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Benjamin C Musall
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rania M M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abeer H Abdelhafez
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lumarie Santiago
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tanya W Moseley
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hagar S Mahmoud
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nabil A Elshafeey
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shu Zhang
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alastair M Thompson
- Division of Surgical Oncology, Baylor College of Medicine, Houston, Texas, USA
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stacy L Moulder
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark D Pagel
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14122860. [PMID: 35740526 PMCID: PMC9220825 DOI: 10.3390/cancers14122860] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
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Jayaprakasam VS, Paroder V, Gibbs P, Bajwa R, Gangai N, Sosa RE, Petkovska I, Golia Pernicka JS, Fuqua JL, Bates DDB, Weiser MR, Cercek A, Gollub MJ. MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. Eur Radiol 2022; 32:971-980. [PMID: 34327580 PMCID: PMC9018044 DOI: 10.1007/s00330-021-08144-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/11/2021] [Accepted: 06/02/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To interrogate the mesorectal fat using MRI radiomics feature analysis in order to predict clinical outcomes in patients with locally advanced rectal cancer. METHODS This retrospective study included patients who underwent neoadjuvant chemoradiotherapy for locally advanced rectal cancer from 2009 to 2015. Three radiologists independently segmented mesorectal fat on baseline T2-weighted axial MRI. Radiomics features were extracted from segmented volumes and calculated using CERR software, with adaptive synthetic sampling being employed to combat large class imbalances. Outcome variables included pathologic complete response (pCR), local recurrence, distant recurrence, clinical T-category (cT), post-treatment T category (ypT), and post-treatment N category (ypN). A maximum of eight most important features were selected for model development using support vector machines and fivefold cross-validation to predict each outcome parameter via elastic net regularization. Diagnostic metrics of the final models were calculated, including sensitivity, specificity, PPV, NPV, accuracy, and AUC. RESULTS The study included 236 patients (54 ± 12 years, 135 men). The AUC, sensitivity, specificity, PPV, NPV, and accuracy for each clinical outcome were as follows: for pCR, 0.89, 78.0%, 85.1%, 52.5%, 94.9%, 83.9%; for local recurrence, 0.79, 68.3%, 80.7%, 46.7%, 91.2%, 78.3%; for distant recurrence, 0.87, 80.0%, 88.4%, 58.3%, 95.6%, 87.0%; for cT, 0.80, 85.8%, 56.5%, 89.1%, 49.1%, 80.1%; for ypN, 0.74, 65.0%, 80.1%, 52.7%, 87.0%, 76.3%; and for ypT, 0.86, 81.3%, 84.2%, 96.4%, 46.4%, 81.8%. CONCLUSION Radiomics features of mesorectal fat can predict pathological complete response and local and distant recurrence, as well as post-treatment T and N categories. KEY POINTS • Mesorectal fat contains important prognostic information in patients with locally advanced rectal cancer (LARC). • Radiomics features of mesorectal fat were significantly different between those who achieved complete vs incomplete pathologic response (accuracy 83.9%, 95% CI: 78.6-88.4%). • Radiomics features of mesorectal fat were significantly different between those who did vs did not develop local or distant recurrence (accuracy 78.3%, 95% CI: 72.0-83.7% and 87.0%, 95% CI: 81.6-91.2% respectively).
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Affiliation(s)
- Vetri Sudar Jayaprakasam
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Raazi Bajwa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Ramon E Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Jennifer S Golia Pernicka
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - James Louis Fuqua
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - David D B Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Martin R Weiser
- Colorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Andrea Cercek
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Marc J Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer. J Pers Med 2021; 11:jpm11101029. [PMID: 34683170 PMCID: PMC8540268 DOI: 10.3390/jpm11101029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/07/2021] [Accepted: 10/13/2021] [Indexed: 12/12/2022] Open
Abstract
We investigated whether textural parameters of peritumoral breast adipose tissue (AT) based on F-18 fluorodeoxyglucose (FDG) PET/CT could predict axillary lymph node metastasis in patients with breast cancer. A total of 326 breast cancer patients with preoperative FDG PET/CT were retrospectively enrolled. PET/CT images were visually assessed and the maximum FDG uptake of axillary lymph nodes (LN SUVmax) was measured. From peritumoral breast AT, 38 textural features of PET imaging were extracted. The diagnostic ability of PET based on visual analysis, LN SUVmax, and textural features of peritumoral breast AT for predicting axillary lymph node metastasis were assessed using the area under the receiver operating characteristic curve (AUC) values. Among the 38 peritumoral breast AT textural features, grey-level co-occurrence matrix (GLCM) entropy showed the highest AUC value (0.830) for predicting axillary lymph node metastasis. The value of GLCM entropy was higher than that of visual analysis (0.739; p < 0.05) and the AUC value was comparable to that of LN SUVmax (0.793; p > 0.05). In the subgroup analysis of patients with negative findings on visual analysis, GLCM entropy still showed a high diagnostic ability (AUC: 0.759) in predicting lymph node metastasis. The findings suggest a potential diagnostic role of PET/CT imaging features of peritumoral breast AT in predicting axillary lymph node metastasis in patients with breast cancer.
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Hussain L, Huang P, Nguyen T, Lone KJ, Ali A, Khan MS, Li H, Suh DY, Duong TQ. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response. Biomed Eng Online 2021; 20:63. [PMID: 34183038 PMCID: PMC8240261 DOI: 10.1186/s12938-021-00899-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/09/2021] [Indexed: 12/02/2022] Open
Abstract
Purpose This study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy. Materials and method This study employed a subset of patients (N = 166) with PCR data from the I-SPY-1 TRIAL (2002–2006). This cohort consisted of patients with stage 2 or 3 breast cancer that underwent anthracycline–cyclophosphamide and taxane treatment. Magnetic resonance imaging (MRI) was acquired pre-neoadjuvant chemotherapy, early, and mid-treatment. Texture features were extracted from post-contrast-enhanced MRI, pre- and post-contrast subtraction images, and with morphological dilation to include peri-tumoral tissue. Molecular subtypes and Ki67 were also included in the prediction model. Performance of classification models used the receiver operating characteristics curve analysis including area under the curve (AUC). Statistical analysis was done using unpaired two-tailed t-tests. Results Molecular subtypes alone yielded moderate prediction performance of PCR (AUC = 0.82, p = 0.07). Pre-, early, and mid-treatment data alone yielded moderate performance (AUC = 0.88, 0.72, and 0.78, p = 0.03, 0.13, 0.44, respectively). The combined pre- and early treatment data markedly improved performance (AUC = 0.96, p = 0.0003). Addition of molecular subtypes improved performance slightly for individual time points but substantially for the combined pre- and early treatment (AUC = 0.98, p = 0.0003). The optimal morphological dilation was 3–5 pixels. Subtraction of post- and pre-contrast MRI further improved performance (AUC = 0.98, p = 0.00003). Finally, among the machine-learning algorithms evaluated, the RUSBoosted Tree machine-learning method yielded the highest performance. Conclusion AI-classification of texture features from MRI of breast tumor at multiple treatment time points accurately predicts eventual PCR. Longitudinal changes in texture features and peri-tumoral features further improve PCR prediction performance. Accurate assessment of treatment efficacy early on could minimize unnecessary toxic chemotherapy and enable mid-treatment modification for patients to achieve better clinical outcomes.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science & IT, Neelum Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan.,Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan.,Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.,Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY, 10467, USA
| | - Pauline Huang
- Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Tony Nguyen
- Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Kashif J Lone
- Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Amjad Ali
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Muhammad Salman Khan
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Haifang Li
- Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Doug Young Suh
- College of Electronics and Convergence Engineering, Kyung Hee University, Seoul, South Korea.
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY, 10467, USA
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10
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Singh A, Chitalia R, Kontos D. Radiogenomics in brain, breast, and lung cancer: opportunities and challenges. J Med Imaging (Bellingham) 2021; 8:031907. [PMID: 34164563 PMCID: PMC8212946 DOI: 10.1117/1.jmi.8.3.031907] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 06/04/2021] [Indexed: 01/06/2023] Open
Abstract
The field of radiogenomics largely focuses on developing imaging surrogates for genomic signatures and integrating imaging, genomic, and molecular data to develop combined personalized biomarkers for characterizing various diseases. Our study aims to highlight the current state-of-the-art and the role of radiogenomics in cancer research, focusing mainly on solid tumors, and is broadly divided into four sections. The first section reviews representative studies that establish the biologic basis of radiomic signatures using gene expression and molecular profiling information. The second section includes studies that aim to non-invasively predict molecular subtypes of tumors using radiomic signatures. The third section reviews studies that evaluate the potential to augment the performance of established prognostic signatures by combining complementary information encoded by radiomic and genomic signatures derived from cancer tumors. The fourth section includes studies that focus on ascertaining the biological significance of radiomic phenotypes. We conclude by discussing current challenges and opportunities in the field, such as the importance of coordination between imaging device manufacturers, regulatory organizations, health care providers, pharmaceutical companies, academic institutions, and physicians for the effective standardization of the results from radiogenomic signatures and for the potential use of these findings to improve precision care for cancer patients.
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Affiliation(s)
- Apurva Singh
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Rhea Chitalia
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Despina Kontos
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
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11
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Yang Y, Zou X, Wang Y, Ma X. Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non-muscle-invasive bladder cancer with CT. Eur J Radiol 2021; 139:109666. [PMID: 33798819 DOI: 10.1016/j.ejrad.2021.109666] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 02/22/2021] [Accepted: 03/13/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To construct a deep-learning convolution neural network (DL-CNN) system for the differentiation of muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) on contrast-enhanced computed tomography (CT) images in patients with bladder cancer. MATERIALS AND METHODS A total of 1200 cross-sectional CT images were obtained from 369 patients with bladder cancer receiving radical cystectomy from January 2015 to June 2018, including 249 non-muscle-invasive bladder cancer (NMIBC) series and 120 muscle-invasive bladder cancer (MIBC) series. All eligible images were distributed randomly into the training, validation, and testing cohorts with ratios of 70 %, 15 %, and 15 %, respectively. We developed one small DL-CNN containing four convolutional and max pooling layers and eight DL-CNNs with pretrained bases from the ImageNet dataset to differentiate NMIBC from MIBC. The intermediate activations were applied on the test dataset to visualize how successive DL-CNN layers transform their input. RESULTS The area under the receiver operating characteristic curve (AUROC) of the validation and testing datasets for the small DL-CNN was 0.946 and 0.998, respectively. The AUROCs of eight deep learning algorithms with pretrained bases ranged from 0.762 to 0.997 in the testing dataset. The VGG16 model had the largest AUROC of 0.997 among the eight algorithms with a sensitivity and specificity of 0.889 and 0.989. The independent features encoded by the small DL-CNN filters were displayed as assemblies of individual channels. CONCLUSION Based on contrast-enhanced CT images, our DL-CNN system could successfully classify NMIBC and MIBC with favorable AUROC in patients with bladder cancer. The application of our system in early stage might assist the pathological examination for the improvement of diagnostic accuracy.
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Affiliation(s)
- Yuhan Yang
- West China School of Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, Sichuan, China.
| | - Xiuhe Zou
- West China Hospital, Sichuan University, Guoxue Road 37, Chengdu, 610041, China.
| | - Yixi Wang
- West China School of Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, Sichuan, China.
| | - Xuelei Ma
- Department of Biotherapy and Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu, 610041, China.
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Tao W, Lu M, Zhou X, Montemezzi S, Bai G, Yue Y, Li X, Zhao L, Zhou C, Lu G. Machine Learning Based on Multi-Parametric MRI to Predict Risk of Breast Cancer. Front Oncol 2021; 11:570747. [PMID: 33718131 PMCID: PMC7952867 DOI: 10.3389/fonc.2021.570747] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 01/18/2021] [Indexed: 01/22/2023] Open
Abstract
Purpose Machine learning (ML) can extract high-throughput features of images to predict disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model to predict the risk of breast cancer. Methods The mpMRI included non-enhanced and enhanced T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), K trans, K ep, V e, and V p. Regions of interest were annotated in an enhanced T1WI map and mapped to other maps in every slice. 1,132 features and top-10 principal components were extracted from every parameter map. Single-parametric and multi-parametric ML models were constructed via 10 rounds of five-fold cross-validation. The model with the highest area under the curve (AUC) was considered as the optimal model and validated by calibration curve and decision curve. Nomogram was built with the optimal ML model and patients' characteristics. Results This study involved 144 malignant lesions and 66 benign lesions. The average age of patients with benign and malignant lesions was 42.5 years old and 50.8 years old, respectively, which were statistically different. The sixth and fourth principal components of K trans had more importance than others. The AUCs of K trans, K ep, V e and V p, non-enhanced T1WI, enhanced T1WI, T2WI, and ADC models were 0.86, 0.81, 0.81, 0.83, 0.79, 0.81, 0.84, and 0.83 respectively. The model with an AUC of 0.90 was considered as the optimal model which was validated by calibration curve and decision curve. Nomogram for the prediction of breast cancer was built with the optimal ML models and patient age. Conclusion Nomogram could improve the ability of breast cancer prediction preoperatively.
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Affiliation(s)
- Weijing Tao
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.,Department of Nuclear Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Mengjie Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xiaoyu Zhou
- Faculty of Mechanical Electronic and Information Engineering, Jiangsu Vocational College of Finance and Economics, Huai'an, China
| | - Stefania Montemezzi
- Radiology Unit, Department of Pathology and Diagnostics, Azienda Ospedaliera Universitaria Integrata-Verona, Verona, Italy
| | - Genji Bai
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Yangming Yue
- Deepwise AI Laboratory, Deepwise Inc., Beijing, China
| | - Xiuli Li
- Deepwise AI Laboratory, Deepwise Inc., Beijing, China
| | - Lun Zhao
- Deepwise AI Laboratory, Deepwise Inc., Beijing, China
| | - Changsheng Zhou
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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The Tumor-Fat Interface Volume of Breast Cancer on Pretreatment MRI Is Associated with a Pathologic Response to Neoadjuvant Chemotherapy. BIOLOGY 2020; 9:biology9110391. [PMID: 33182628 PMCID: PMC7697338 DOI: 10.3390/biology9110391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 11/07/2020] [Indexed: 12/31/2022]
Abstract
Simple Summary Contact between a tumor and the adjacent fat is a potential biomarker to predict the therapy response in breast cancer, but it has not been quantitatively explored. In this study, we measured the direct contact between the tumor and adjacent fat using breast magnetic resonance imaging with machine learning and found that patients with a greater volume of contact between tumor and fat were less likely to have a complete pathological response. Our results suggest that the volume of the tumor–fat interface is a potential prognostic imaging biomarker to predict the treatment response to neoadjuvant chemotherapy. Abstract Adipocytes are active sources of numerous adipokines that work in both a paracrine and endocrine manner. It is not known that the direct contact between tumor and neighboring fat measured by pretreatment breast magnetic resonance imaging (MRI) affects treatment outcomes to neoadjuvant chemotherapy (NAC) in breast cancer patients. A biomarker quantifying the tumor–fat interface volume from pretreatment MRI was proposed and used to predict pathologic complete response (pCR) in breast cancer patients treated with NAC. The tumor–fat interface volume was computed with data-driven clustering using multiphasic MRI. Our approach was developed and validated in two cohorts consisting of 1140 patients. A high tumor–fat interface volume was significantly associated with a non-pCR in both the development and validation cohorts (p = 0.030 and p = 0.037, respectively). Quantitative measurement of the tumor–fat interface volume based on pretreatment MRI may be useful for precision medicine and subsequently influence the treatment strategy of patients.
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Vaugier L, Ferrer L, Mengue L, Jouglar E. Radiomics for radiation oncologists: are we ready to go? BJR Open 2020; 2:20190046. [PMID: 33178967 PMCID: PMC7594896 DOI: 10.1259/bjro.20190046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/06/2020] [Accepted: 03/09/2020] [Indexed: 12/19/2022] Open
Abstract
Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.
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Affiliation(s)
- Loïg Vaugier
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Ludovic Ferrer
- Department of Medical Physics, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Laurence Mengue
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Emmanuel Jouglar
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
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15
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De Munck TJI, Soeters PB, Koek GH. The role of ectopic adipose tissue: benefit or deleterious overflow? Eur J Clin Nutr 2020; 75:38-48. [PMID: 32801303 DOI: 10.1038/s41430-020-00713-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 07/01/2020] [Accepted: 08/04/2020] [Indexed: 12/11/2022]
Abstract
Ectopic adipose tissues (EAT) are present adjacent to many organs and have predominantly been described in overweight and obesity. They have been suggested to be related to fatty acid overflow and to have harmful effects. The objective of this semi-comprehensive review is to explore whether EAT may play a supportive role rather than interfering with its function, when the adjacent organ is challenged metabolically and functionally. EAT are present adhered to different tissues or organs, including lymph nodes, heart, kidney, ovaries and joints. In this review, we only focused on epicardial, perinodal, and peritumoral fat since these locations have been studied in more detail. Evidence was found that EAT volume significantly increased, associated with chronic metabolic challenges of the corresponding tissue. In vitro evidence revealed transfer of fatty acids from peritumoral and perinodal fat to the adjacent tissue. Cytokine expression in these EAT is upregulated when the adjacent tissue is challenged. In these tissues, glycolysis is enhanced, whereas fatty acid oxidation is increased. Together with more direct evidence, this shows that glucose is oxidized to a lesser degree, but used to support anabolic metabolism of the adjacent tissue. In these situations, browning occurs, resulting from upregulation of anabolic metabolism, stimulated by uncoupling proteins 1 and 2 and possibly 3. In conclusion, the evidence found is fragmented but the available data support the view that accumulation and browning of adipocytes adjacent to the investigated organs or tissues may be a normal physiological response promoting healing and (patho)physiological growth.
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Affiliation(s)
- Toon J I De Munck
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Maastricht University Medical Centre, Maastricht, The Netherlands. .,School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, The Netherlands.
| | - Peter B Soeters
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, The Netherlands.,Department of Surgery, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Ger H Koek
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Maastricht University Medical Centre, Maastricht, The Netherlands.,School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, The Netherlands.,Department of Surgery, Klinikum RWTH Aachen, Aachen, Germany
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Pesapane F, Suter MB, Rotili A, Penco S, Nigro O, Cremonesi M, Bellomi M, Jereczek-Fossa BA, Pinotti G, Cassano E. Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation? Med Oncol 2020; 37:29. [PMID: 32180032 DOI: 10.1007/s12032-020-01353-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 02/26/2020] [Indexed: 02/06/2023]
Abstract
The diagnosis of breast cancer currently relies on radiological and clinical evaluation, confirmed by histopathological examination. However, such approach has some limitations as the suboptimal sensitivity, the long turnaround time for recall tests, the invasiveness of the procedure and the risk that some features of target lesions may remain undetected, making re-biopsy a necessity. Recent technological advances in the field of artificial intelligence hold promise in addressing such medical challenges not only in cancer diagnosis, but also in treatment assessment, and monitoring of disease progression. In the perspective of a truly personalised medicine, based on the early diagnosis and individually tailored treatments, two new technologies, namely radiomics and liquid biopsy, are rising as means to obtain information from diagnosis to molecular profiling and response assessment, without the need of a biopsied tissue sample. Radiomics works through the extraction of quantitative peculiar features of cancer from radiological data, while liquid biopsy gets the whole of the malignancy's biology from something as easy as a blood sample. Both techniques hopefully will identify diagnostic and prognostic information of breast cancer potentially reducing the need for invasive (and often difficult to perform) biopsies and favouring an approach that is as personalised as possible for each patient. Nevertheless, such techniques will not substitute tissue biopsy in the near future, and even in further times they will require the aid of other parameters to be correctly interpreted and acted upon.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy.
| | | | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Olga Nigro
- Medical Oncology, ASST Sette Laghi, Viale Borri 57, 21100, Varese, VA, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Massimo Bellomi
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiology, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Graziella Pinotti
- Medical Oncology, ASST Sette Laghi, Viale Borri 57, 21100, Varese, VA, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
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Zheng J, Kong J, Wu S, Li Y, Cai J, Yu H, Xie W, Qin H, Wu Z, Huang J, Lin T. Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach. Cancer 2019; 125:4388-4398. [PMID: 31469418 DOI: 10.1002/cncr.32490] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 06/09/2019] [Accepted: 08/02/2019] [Indexed: 02/03/2023]
Abstract
BACKGROUND Bladder cancer (BCa) can be divided into muscle-invasive BCa (MIBC) and non-muscle-invasive BCa (NMIBC). Whether the tumor infiltrates the detrusor muscle is a critical determinant of disease management in patients with BCa. However, the current preoperative diagnostic accuracy of muscular invasiveness is less than satisfactory. The authors report a radiomic-clinical nomogram for the individualized preoperative differentiation of MIBC from NMIBC. METHODS In total, 2602 radiomics features were extracted from whole bladder tumors and the basal part of the lesions on T2-weighted magnetic resonance imaging. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set (n = 130). Furthermore, a radiomic-clinical nomogram was developed incorporating the radiomics signature and selected clinical predictors based on a multivariable logistic regression analysis. The performance of the nomogram (discrimination, calibration, and clinical usefulness) was assessed and validated in an independent validation set (n = 69). RESULTS The radiomics signature, consisting of 23 selected features, showed good discrimination in the training and validation sets (area under the curve [AUC], 0.913 and 0.874, respectively). Incorporating the radiomics signature and magnetic resonance imaging-determined tumor size, the radiomic-clinical nomogram showed favorable calibration and discrimination in the training set with an AUC of 0.922, which was confirmed in the validation set (AUC, 0.876). Decision curve analysis and net reclassification improvement and integrated discrimination improvement indices (net reclassification improvement, 0.338, integrated discrimination improvement, 0.385) demonstrated the clinical usefulness of the nomogram. CONCLUSIONS The proposed noninvasive radiomic-clinical nomogram can increase the accuracy of preoperatively discriminating MIBC from NMIBC, which may aid in clinical decision making and improve patient prognosis.
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Affiliation(s)
- Junjiong Zheng
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yong Li
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jinhua Cai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Hao Yu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Weibin Xie
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Haide Qin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jian Huang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
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Lee JW, Kim SY, Lee HJ, Han SW, Lee JE, Lee SM. Prognostic Significance of CT-Attenuation of Tumor-Adjacent Breast Adipose Tissue in Breast Cancer Patients with Surgical Resection. Cancers (Basel) 2019; 11:E1135. [PMID: 31398863 PMCID: PMC6721593 DOI: 10.3390/cancers11081135] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/03/2019] [Accepted: 08/06/2019] [Indexed: 02/06/2023] Open
Abstract
The purpose of this study was to evaluate the prognostic significance of computed tomography (CT)-attenuation of tumor-adjacent breast adipose tissue for predicting recurrence-free survival (RFS) in patients with breast cancer. We retrospectively enrolled 287 breast cancer patients who underwent pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT. From non-contrast-enhanced CT images of PET/CT, CT-attenuation values of tumor-adjacent breast adipose tissue (TAT HU) and contralateral breast adipose tissue (CAT HU) were measured. Difference (HU difference) and percent difference (HU difference %) in CT-attenuation values between TAT HU and CAT HU were calculated. The relationships of these breast adipose tissue parameters with tumor factors and RFS were assessed. TAT HU was significantly higher than CAT HU (p < 0.001). TAT HU, HU difference, and HU difference % showed significant correlations with T stage and estrogen receptor and progesterone receptor status (p < 0.05), whereas CAT HU had no significant relationships with tumor factors (p > 0.05). Patients with high TAT HU, HU difference, and HU difference % had significantly worse RFS than those with low values (p < 0.001). In multivariate analysis, TAT HU and HU difference % were significantly associated with RFS after adjusting for clinico-pathologic factors (p < 0.05). CT-attenuation of tumor-adjacent breast adipose tissue was significantly associated with RFS in patients with breast cancer. The findings seem to support the close contact between breast cancer cells and tumor-adjacent adipocytes observed with imaging studies.
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Affiliation(s)
- Jeong Won Lee
- Department of Nuclear Medicine, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, 25 Simgok-ro 100 beon-gil, Seo-gu, Incheon 22711, Korea
| | - Sung Yong Kim
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan, Chungcheongnam-do 31151, Korea
| | - Hyun Ju Lee
- Department of Pathology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan, Chungcheongnam-do 31151, Korea
| | - Sun Wook Han
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan, Chungcheongnam-do 31151, Korea
| | - Jong Eun Lee
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan, Chungcheongnam-do 31151, Korea
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan, Chungcheongnam-do 31151, Korea.
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Engin AB, Engin A, Gonul II. The effect of adipocyte-macrophage crosstalk in obesity-related breast cancer. J Mol Endocrinol 2019; 62:R201-R222. [PMID: 30620711 DOI: 10.1530/jme-18-0252] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 01/07/2019] [Indexed: 12/11/2022]
Abstract
Adipose tissue is the primary source of many pro-inflammatory cytokines in obesity. Macrophage numbers and pro-inflammatory gene expression are positively associated with adipocyte size. Free fatty acid and tumor necrosis factor-α involve in a vicious cycle between adipocytes and macrophages aggravating inflammatory changes. Thereby, M1 macrophages form a characteristic 'crown-like structure (CLS)' around necrotic adipocytes in obese adipose tissue. In obese women, CLSs of breast adipose tissue are responsible for both increase in local aromatase activity and aggressive behavior of breast cancer cells. Interlinked molecular mechanisms between adipocyte-macrophage-breast cancer cells in obesity involve seven consecutive processes: Excessive release of adipocyte- and macrophage-derived inflammatory cytokines, TSC1-TSC2 complex-mTOR crosstalk, insulin resistance, endoplasmic reticulum (ER) stress and excessive oxidative stress generation, uncoupled respiration and hypoxia, SIRT1 controversy, the increased levels of aromatase activity and estrogen production. Considering elevated risks of estrogen receptor (E2R)-positive postmenopausal breast cancer growth in obesity, adipocyte-macrophage crosstalk is important in the aforementioned issues. Increased mTORC1 signaling in obesity ensures the strong activation of oncogenic signaling in E2Rα-positive breast cancer cells. Since insulin and insulin-like growth factors have been identified as tumor promoters, hyperinsulinemia is an independent risk factor for poor prognosis in breast cancer despite peripheral insulin resistance. The unpredictable effects of adipocyte-derived leptin-estrogen-macrophage axis, and sirtuin 1 (SIRT1)-adipose-resident macrophage axis in obese postmenopausal patients with breast cancer are unresolved mechanistic gaps in the molecular links between the tumor growth and adipocytokines.
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Affiliation(s)
- Ayse Basak Engin
- Department of Toxicology, Faculty of Pharmacy, Gazi University, Ankara, Turkey
| | - Atilla Engin
- Department of General Surgery, Faculty of Medicine, Gazi University, Ankara, Turkey
| | - Ipek Isik Gonul
- Department of Pathology, Faculty of Medicine, Gazi University, Ankara, Turkey
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A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification. AJR Am J Roentgenol 2019; 212:259-270. [DOI: 10.2214/ajr.18.20391] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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21
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Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M. A New Challenge for Radiologists: Radiomics in Breast Cancer. BIOMED RESEARCH INTERNATIONAL 2018; 2018:6120703. [PMID: 30402486 PMCID: PMC6196984 DOI: 10.1155/2018/6120703] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 08/24/2018] [Accepted: 09/09/2018] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Over the last decade, the field of medical imaging experienced an exponential growth, leading to the development of radiomics, with which innumerable quantitative features are obtained from digital medical images, providing a comprehensive characterization of the tumor. This review aims to assess the role of this emerging diagnostic tool in breast cancer, focusing on the ability of radiomics to predict malignancy, response to neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk of recurrence. EVIDENCE ACQUISITION A literature search on PubMed and on Cochrane database websites to retrieve English-written systematic reviews, review articles, meta-analyses, and randomized clinical trials published from August 2013 up to July 2018 was carried out. RESULTS Twenty papers (19 retrospective and 1 prospective studies) conducted with different conventional imaging modalities were included. DISCUSSION The integration of quantitative information with clinical, histological, and genomic data could enable clinicians to provide personalized treatments for breast cancer patients. Current limitations of a routinely application of radiomics are represented by the limited knowledge of its basics concepts among radiologists and by the lack of efficient and standardized systems of feature extraction and data sharing.
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Affiliation(s)
- Paola Crivelli
- Department of Biomedical Sciences, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Roberta Eufrasia Ledda
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Nicola Parascandolo
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Alberto Fara
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Daniela Soro
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Maurizio Conti
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
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Chen JH, Zhang Y, Chan S, Chang RF, Su MY. Quantitative analysis of peri-tumor fat in different molecular subtypes of breast cancer. Magn Reson Imaging 2018; 53:34-39. [PMID: 29969646 DOI: 10.1016/j.mri.2018.06.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 06/13/2018] [Accepted: 06/28/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSES The aim of this study was to develop morphological analytic methods to analyze the tumor-fat interface and in different peritumoral shells away from the tumor, and to compare the results among three molecular subtypes of breast cancer. MATERIALS AND METHODS A total of 102 women (mean age 48.5 y/o) with solitary well-defined breast cancers were analyzed, including 46 human epidermal growth factor receptor 2 (HER2) (+), 46 HER2(-) hormonal receptor (HR) (+), and 10 triple negative (TN) breast cancers. The tumor lesion, the breast, the fibroglandular and fatty tissue were segmented using well-established methods. The whole breast fat percentage and the peri-tumor interface fat percentage were measured. Three shells (SH1, SH2, SH3) surrounding the convex hall of the three dimensional (3D) tumor were defined and in each shell the volumetric percentage of fat was calculated. The peri-tumor interface fat percentage and the volumetric percentage of fat in the three peri-tumoral shells were compared among different subtypes. RESULTS In the TN group, the fat percentage on the tumor boundary was 43 ± 20% and 78 ± 12% for two dimensional (2D) and 3D measurement, respectively, which were the highest among the three subtypes but not significantly different. The fat percentage in SH2 and SH3 in the TN group was 82 ± 7% and 85 ± 7%, which was significantly higher compared to the two other two subtypes. The results remained after controlling for the whole breast fat percentage. CONCLUSIONS This study provided a feasible method for quantitative analysis of peri-tumoral tissue characteristics. Because of small patient number, the finding that TN tumors had the highest peri-tumor fat content among the three subtypes needs to be further verified with a large cohort study.
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Affiliation(s)
- Jeon-Hor Chen
- Center For Functional Onco-Imaging of Department of Radiological Sciences, University of California, Irvine, CA, USA; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan.
| | - Yang Zhang
- Center For Functional Onco-Imaging of Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Siwa Chan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Medical Imaging, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan; Department of Radiology, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Ruey-Feng Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Min-Ying Su
- Center For Functional Onco-Imaging of Department of Radiological Sciences, University of California, Irvine, CA, USA
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Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J 2018; 9:77-102. [PMID: 29515689 PMCID: PMC5833337 DOI: 10.1007/s13167-018-0128-8] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 01/29/2018] [Indexed: 02/06/2023]
Abstract
Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.
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Affiliation(s)
- Miaolong Lu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Xianquan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- The State Key Laboratory of Medical Genetics, Central South University, 88 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
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Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS. Rapid review: radiomics and breast cancer. Breast Cancer Res Treat 2018; 169:217-229. [PMID: 29396665 DOI: 10.1007/s10549-018-4675-4] [Citation(s) in RCA: 161] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/16/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE To perform a rapid review of the recent literature on radiomics and breast cancer (BC). METHODS A rapid review, a streamlined approach to systematically identify and summarize emerging studies was done (updated 27 September 2017). Clinical studies eligible for inclusion were those that evaluated BC using a radiomics approach and provided data on BC diagnosis (detection or characterization) or BC prognosis (response to therapy, morbidity, mortality), or provided data on technical challenges (software application: open source, repeatability of results). Descriptive statistics, results, and radiomics quality score (RQS) are presented. RESULTS N = 17 retrospective studies, all published after 2015, provided BC-related radiomics data on 3928 patients evaluated with a radiomics approach. Most studies were done for diagnosis and/or characterization (65%, 11/17) or to aid in prognosis (41%, 7/17). The mean number of radiomics features considered was 100. Mean RQS score was 11.88 ± 5.8 (maximum value 36). The RQS criteria related to validation, gold standard, potential clinical utility, cost analysis, and open science data had the lowest scores. The majority of studies n = 16/17 (94%) provided correlation with histological outcomes and staging variables or biomarkers. Only 4/17 (23%) studies provided evidence of correlation with genomic data. Magnetic resonance imaging (MRI) was used in most studies n = 14/17 (82%); however, ultrasound (US), mammography, or positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F FDG PET/CT) was also used. Much heterogeneity was found for software usage. CONCLUSIONS The study of radiomics in BC patients is a new and emerging translational research topic. Radiomics in BC is frequently done to potentially improve diagnosis and characterization, mostly using MRI. Substantial quality limitations were found; high-quality prospective and reproducible studies are needed to further potential application.
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Affiliation(s)
- Francesca Valdora
- Department of Health Sciences, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy
| | - Nehmat Houssami
- Sydney School of Public Health, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Federica Rossi
- Department of Health Sciences, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy
| | | | - Alberto Stefano Tagliafico
- Department of Health Sciences, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy. .,Ospedale Policlinico San Martino IST, Genoa, Italy.
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diFlorio Alexander RM, Haider SJ, MacKenzie T, Goodrich ME, Weiss J, Onega T. Correlation between obesity and fat-infiltrated axillary lymph nodes visualized on mammography. Br J Radiol 2018; 91:20170110. [PMID: 29144164 DOI: 10.1259/bjr.20170110] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
OBJECTIVE Using screening mammography, this study investigated the association between obesity and axillary lymph node (LN) size and morphology. METHODS We conducted a retrospective review of 188 females who underwent screening mammography at an academic medical centre. Length and width of the LN and hilum were measured in the largest, mammographically visible axillary node. The hilo-cortical ratio (HCR) was calculated as the hilar width divided by the cortical width. Measurements were performed by a board certified breast radiologist and a resident radiology physician. Inter-rater agreement was assessed with Pearson correlation coefficient. We performed multivariable regression analysis for associations of LN measurements with body mass index (BMI), breast density and age. RESULTS There was a strong association between BMI and LN dimensions, hilum dimensions and HCR (p < 0.001 for all metrics). There was no significant change in cortex width with increasing BMI (p = 0.15). Increases in LN length and width were found with increasing BMI [0.6 mm increase in length per unit BMI, 95% CI (0.4-0.8), p < 0.001 and0.3 mm increase in width per unit BMI, 95% CI(0.2-0.4), p < 0.001, respectively]. Inter-rater reliability for lymph node and hilum measurements was 0.57-0.72. CONCLUSION We found a highly significant association between increasing BMI and axillary LN dimensions independent of age and breast density with strong interobserver agreement. The increase in LN size was driven by expansion of the LN hilum secondary to fat infiltration. Advances in knowledge: This preliminary work determined a relationship between fat infiltrated axillary lymph nodes and obesity.
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Affiliation(s)
| | - Steffen J Haider
- 2 Department of Radiology, New York Presbyterian Hospital/Columbia University Medical Center , New York, NY , USA
| | - Todd MacKenzie
- 2 Department of Radiology, New York Presbyterian Hospital/Columbia University Medical Center , New York, NY , USA
| | - Martha E Goodrich
- 3 Department of Biomedical Data Science, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth One Medical Center Drive , Lebanon, NH , USA
| | - Julie Weiss
- 3 Department of Biomedical Data Science, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth One Medical Center Drive , Lebanon, NH , USA
| | - Tracy Onega
- 3 Department of Biomedical Data Science, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth One Medical Center Drive , Lebanon, NH , USA
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Jordan BF, Gourgue F, Cani PD. Adipose Tissue Metabolism and Cancer Progression: Novel Insights from Gut Microbiota? CURRENT PATHOBIOLOGY REPORTS 2017; 5:315-322. [PMID: 29188139 PMCID: PMC5684272 DOI: 10.1007/s40139-017-0154-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose of Review Obesity is strongly associated with the development of several types of cancers. This review aims to discuss the recent key mechanisms and actors underlying the link between adipose tissue metabolism and cancer, and the unequivocal common mechanisms connecting gut microbes to adipose tissue and eventually cancer development. Recent Findings Complex interactions among systemic and tissue-specific pathways are suggested to link obesity and cancer, involving endocrine hormones, adipokines, fatty acids, inflammation, metabolic alterations, and hypoxia. Emerging evidence also suggests that the gut microbiota, another key environmental factor, may be considered as a converging element. Studies have shown that cancer susceptibility may be induced in germ-free mice colonized with the gut microbiota from high-fat diet-fed mice. Suggested mechanisms may involve inflammation, immunity changes, lipogenic substrates, and adipogenesis. Summary Cancer development is a complex process that may be under the control of previously unthought factors such as the gut microbiota. Whether specific intervention targeting the gut microbiota may reduce adipose tissue-driven cancer is an interesting strategy that remains to be proven.
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Affiliation(s)
- Benedicte F Jordan
- Louvain Drug Research Institute, Biomedical Magnetic Resonance Research group, Université Catholique de Louvain, Av. E. Mounier, 73, B1.73.08, 1200 Brussels, Belgium
| | - Florian Gourgue
- Louvain Drug Research Institute, Biomedical Magnetic Resonance Research group, Université Catholique de Louvain, Av. E. Mounier, 73, B1.73.08, 1200 Brussels, Belgium.,Louvain Drug Research Institute, WELBIO (Walloon Excellence in Life Sciences and BIOtechnology), Metabolism and Nutrition Research group, Université Catholique de Louvain, Av. E. Mounier, 73 box B1.73.11, 1200 Brussels, Belgium
| | - Patrice D Cani
- Louvain Drug Research Institute, WELBIO (Walloon Excellence in Life Sciences and BIOtechnology), Metabolism and Nutrition Research group, Université Catholique de Louvain, Av. E. Mounier, 73 box B1.73.11, 1200 Brussels, Belgium
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Zhang B, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, Huang W, Zhang S. Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget 2017; 8:72457-72465. [PMID: 29069802 PMCID: PMC5641145 DOI: 10.18632/oncotarget.19799] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 06/28/2017] [Indexed: 01/03/2023] Open
Abstract
We aimed to investigate the potential of radiomic features of magnetic resonance imaging (MRI) to predict progression in patients with advanced nasopharyngeal carcinoma (NPC). One hundred and thirteen consecutive patients (01/2007-07/2013) (training cohort: n = 80; validation cohort: n = 33) with advanced NPC were enrolled. A total of 970 initial features were extracted from T2-weighted (T2-w) (n = 485) and contrast-enhanced T1-weighted (CET1-w) MRI (n = 485) for each patient. We used least absolute shrinkage and selection operator (Lasso) method to select features that were most significantly associated with the progression. The selected features were used to construct radiomics-based models and the predictive performance of which were assessed with respect to the area under the curve (AUC). As a result, eight features significantly associated with the progression of advanced NPC were identified. In the training cohort, a radiomic model based on combined CET1-w and T2-w images (AUC: 0.886, 95%CI: 0.815-0.956) demonstrated better prognostic performance than models based on CET1-w (AUC: 0.793, 95%CI: 0.698-0.889) or T2-w images alone (AUC: 0.813, 95%CI: 0.721-0.904). These results were confirmed in the validation cohort. Accordingly, MRI-based radiomic biomarkers present high accuracy in the pre-treatment prediction of progression in advanced NPC.
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Affiliation(s)
- Bin Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China.,Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China
| | - Fusheng Ouyang
- Department of Radiology, The First People's Hospital of Shunde, Foshan, P.R. China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, P.R. China
| | - Yuhao Dong
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Lu Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Xiaokai Mo
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Wenhui Huang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China
| | - Shuixing Zhang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China.,Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China
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Peeken JC, Nüsslin F, Combs SE. "Radio-oncomics" : The potential of radiomics in radiation oncology. Strahlenther Onkol 2017; 193:767-779. [PMID: 28687979 DOI: 10.1007/s00066-017-1175-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 06/19/2017] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. METHODS After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. RESULTS Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. DISCUSSION Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. CONCLUSION This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.
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Affiliation(s)
- Jan Caspar Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany.
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany
- Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
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Lee G, Lee HY, Ko ES, Jeong WK. Radiomics and imaging genomics in precision medicine. PRECISION AND FUTURE MEDICINE 2017. [DOI: 10.23838/pfm.2017.00101] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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