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Campana A, Gandomkar Z, Giannotti N, Reed W. The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review. J Med Radiat Sci 2023; 70:462-478. [PMID: 37534540 PMCID: PMC10715343 DOI: 10.1002/jmrs.709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/16/2023] [Indexed: 08/04/2023] Open
Abstract
Radiomics is an emerging field that aims to extract and analyse a comprehensive set of quantitative features from medical images. This scoping review is focused on MRI-based radiomic features for the molecular profiling of breast tumours and the implications of this work for predicting patient outcomes. A thorough systematic literature search and outcome extraction were performed to identify relevant studies published in MEDLINE/PubMed (National Centre for Biotechnology Information), EMBASE and Scopus from 2015 onwards. The following information was retrieved from each article: study purpose, study design, extracted radiomic features, machine learning technique(s), sample size/characteristics, statistical result(s) and implications on patient outcomes. Based on the study purpose, four key themes were identified in the included 63 studies: tumour subtype classification (n = 35), pathologically complete response (pCR) prediction (n = 15), lymph node metastasis (LNM) detection (n = 7) and recurrence rate prediction (n = 6). In all four themes, reported accuracies widely varied among the studies, for example, area under receiver characteristics curve (AUC) for detecting LNM ranged from 0.72 to 0.91 and the AUC for predicting pCR ranged from 0.71 to 0.99. In all four themes, combining radiomic features with clinical data improved the predictive models. Preliminary results of this study showed radiomics potential to characterise the whole tumour heterogeneity, with clear implications for individual-targeted treatment. However, radiomics is still in the pre-clinical phase, currently with an insufficient number of large multicentre studies and those existing studies are often limited by insufficient methodological transparency and standardised workflow. Consequently, the clinical translation of existing studies is currently limited.
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Affiliation(s)
- Annalise Campana
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Ziba Gandomkar
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Nicola Giannotti
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
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Kim MY, Yoen H, Ji H, Park SJ, Kim SM, Han W, Cho N. Ultrafast MRI and T1 and T2 Radiomics for Predicting Invasive Components in Ductal Carcinoma in Situ Diagnosed With Percutaneous Needle Biopsy. Korean J Radiol 2023; 24:1190-1199. [PMID: 38016679 DOI: 10.3348/kjr.2023.0208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 07/26/2023] [Accepted: 09/05/2023] [Indexed: 11/30/2023] Open
Abstract
OBJECTIVE This study aimed to investigate the feasibility of ultrafast magnetic resonance imaging (MRI) and radiomic features derived from breast MRI for predicting the upstaging of ductal carcinoma in situ (DCIS) diagnosed using percutaneous needle biopsy. MATERIALS AND METHODS Between August 2018 and June 2020, 95 patients with 98 DCIS lesions who underwent preoperative breast MRI, including an ultrafast sequence, and subsequent surgery were included. Four ultrafast MRI parameters were analyzed: time-to-enhancement, maximum slope (MS), area under the curve for 60 s after enhancement, and time-to-peak enhancement. One hundred and seven radiomic features were extracted for the whole tumor on the first post-contrast T1WI and T2WI using PyRadiomics. Clinicopathological characteristics, ultrafast MRI findings, and radiomic features were compared between the pure DCIS and DCIS with invasion groups. Prediction models, incorporating clinicopathological, ultrafast MRI, and radiomic features, were developed. Receiver operating characteristic curve analysis and area under the curve (AUC) were used to evaluate model performance in distinguishing between the two groups using leave-one-out cross-validation. RESULTS Thirty-six of the 98 lesions (36.7%) were confirmed to have invasive components after surgery. Compared to the pure DCIS group, the DCIS with invasion group had a higher nuclear grade (P < 0.001), larger mean lesion size (P = 0.038), larger mean MS (P = 0.002), and different radiomic-related characteristics, including a more extensive tumor volume; higher maximum gray-level intensity; coarser, more complex, and heterogeneous texture; and a greater concentration of high gray-level intensity. No significant differences in AUCs were found between the model incorporating nuclear grade and lesion size (0.687) and the models integrating additional ultrafast MRI and radiomic features (0.680-0.732). CONCLUSION High nuclear grade, larger lesion size, larger MS, and multiple radiomic features were associated with DCIS upstaging. However, the addition of MS and radiomic features to the prediction model did not significantly improve the prediction performance.
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Affiliation(s)
- Min Young Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Heera Yoen
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hye Ji
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang Joon Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- MEDICALIP Co. Ltd., Seoul, Republic of Korea
| | - Sun Mi Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Wonshik Han
- Department of Surgery and Cancer Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
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Huang Y, Guo Y, Xiao Q, Liang S, Yu Q, Qian L, Zhou J, Le J, Pei Y, Wang L, Chang C, Chen S, Zhou S. Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer. BREAST CANCER (DOVE MEDICAL PRESS) 2023; 15:461-472. [PMID: 37456987 PMCID: PMC10349575 DOI: 10.2147/bctt.s408997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/01/2023] [Indexed: 07/18/2023]
Abstract
Purpose The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures. Patients and Methods Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features. Results Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC. Conclusion The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.
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Affiliation(s)
- Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Yi Guo
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Qin Xiao
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, People’s Republic of China
| | - Shuyu Liang
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Qiang Yu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Lang Qian
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Jin Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Jian Le
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Yuchen Pei
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Lei Wang
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Sheng Chen
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Shichong Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
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AĞLAMIŞ S, BAYKARA M. Meme manyetik rezonans görüntülemede malign ve benign lezyonların ayrımında histogram analizi: ön çalışma. CUKUROVA MEDICAL JOURNAL 2022. [DOI: 10.17826/cumj.1090183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Purpose: The present study assesses whether malignant and benign lesions can be distinguished through histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted breast magnetic resonance images (MRIs).
Materials and Methods: MRIs of 20 malignant and 20 benign breast lesions were reviewed retrospectively by histogram analysis performed using Osirix V.4.9 software. The regions of interest (ROIs) were drawn manually to include almost the entire lesion, and values from these ROIs were used to calculate gray-level intensity mean, standard deviation, entropy, uniformity, skewness, kurtosis, and percentile values.
Results: In non-fat-suppressed T1-weighted images, the minimum, 1st, 3rd, 5th, 10th and 25th percentile values were significantly lower in the malignant lesions than in the benign lesions. The minimum value had sensitivity of 70% and specificity of 63.2%. On the fat-suppressed T2-weighted images, skewness was significantly higher while uniformity was significantly lower in malignant lesions than benign lesions. Skewness had 68.4% sensitivity and 60% specificity, and uniformity had 65% sensitivity and 68.4% specificity.
Conclusion: The results of this study demonstrated that histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted images can be used to differentiate malignant and benign lesions in breast MRI.
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Lee J, Kim SH, Kim Y, Park J, Park GE, Kang BJ. Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer. Cancers (Basel) 2022; 14:cancers14184461. [PMID: 36139620 PMCID: PMC9497155 DOI: 10.3390/cancers14184461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/04/2022] [Accepted: 09/11/2022] [Indexed: 11/16/2022] Open
Abstract
This study aimed to predict early breast cancer recurrence in women under 40 years of age using radiomics signature and clinicopathological information. We retrospectively investigated 155 patients under 40 years of age with invasive breast cancer who underwent MRI and surgery. Through stratified random sampling, 111 patients were assigned as the training set, and 44 were assigned as the validation set. Recurrence-associated factors were investigated based on recurrence within 5 years during the total follow-up period. A Rad-score was generated through texture analysis (3D slicer, ver. 4.8.0) of breast MRI using the least absolute shrinkage and selection operator Cox regression model. The Rad-score showed a significant association with disease-free survival (DFS) in the training set (p = 0.003) and validation set (p = 0.020) in the Kaplan–Meier analysis. The nomogram was generated through Cox proportional hazards models, and its predictive ability was validated. The nomogram included the Rad-score and estrogen receptor negativity as predictive factors and showed fair DFS predictive ability in both the training and validation sets (C-index 0.63, 95% CI 0.45–0.79). In conclusion, the Rad-score can predict the disease recurrence of invasive breast cancer in women under 40 years of age, and the Rad-score-based nomogram showed reasonably high DFS predictive ability, especially within 2 years of surgery.
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Affiliation(s)
- Jeongmin Lee
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Sung Hun Kim
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
- Correspondence: ; Tel.: +82-2-2258-6250
| | - Yelin Kim
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
| | - Jaewoo Park
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
- Department of Applied Statistics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
| | - Ga Eun Park
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Bong Joo Kang
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
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Derouane F, van Marcke C, Berlière M, Gerday A, Fellah L, Leconte I, Van Bockstal MR, Galant C, Corbet C, Duhoux FP. Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine. Cancers (Basel) 2022; 14:3876. [PMID: 36010869 PMCID: PMC9405974 DOI: 10.3390/cancers14163876] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/05/2022] [Accepted: 08/09/2022] [Indexed: 02/07/2023] Open
Abstract
Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.
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Affiliation(s)
- Françoise Derouane
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Medical Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Cédric van Marcke
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Medical Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Martine Berlière
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Gynecology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Gynecology (GYNE), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Amandine Gerday
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Gynecology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Latifa Fellah
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Radiology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Isabelle Leconte
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Radiology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Mieke R. Van Bockstal
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Pathology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Christine Galant
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Pathology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Cyril Corbet
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Pharmacology and Therapeutics (FATH), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Francois P. Duhoux
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Medical Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
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Mao Y, Xiong Z, Wu S, Huang Z, Zhang R, He Y, Peng Y, Ye Y, Dong T, Mai H. The Predictive Value of Magnetic Resonance Imaging-based Texture Analysis in Evaluating Histopathological Grades of Breast Phyllodes Tumor. J Breast Cancer 2022; 25:117-130. [PMID: 35506580 PMCID: PMC9065359 DOI: 10.4048/jbc.2022.25.e14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 02/04/2022] [Accepted: 03/13/2022] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Knowing the distinction between benign and borderline/malignant phyllodes tumors (PTs) can help in the surgical treatment course. Herein, we investigated the value of magnetic resonance imaging-based texture analysis (MRI-TA) in differentiating between benign and borderline/malignant PTs. METHODS Forty-three women with 44 histologically proven PTs underwent breast MRI before surgery and were classified into benign (n = 26) and borderline/malignant groups (n = 18 [15 borderline, 3 malignant]). Clinical and routine MRI parameters (CRMP) and MRI-TA were used to distinguish benign from borderline/malignant PT. In total, 298 texture parameters were extracted from fat-suppression (FS) T2-weighted, FS unenhanced T1-weighted, and FS first-enhanced T1-weighted sequences. To evaluate the diagnostic performance, receiver operating characteristic curve analysis was performed for the K-nearest neighbor classifier trained with significantly different parameters of CRMP, MRI sequence-based TA, and the combination strategy. RESULTS Compared with benign PTs, borderline/malignant ones presented a higher local recurrence (p = 0.045); larger size (p < 0.001); different time-intensity curve pattern (p = 0.010); and higher frequency of strong lobulation (p = 0.024), septation enhancement (p = 0.048), cystic component (p = 0.023), and irregular cystic wall (p = 0.045). TA of FS T2-weighted images (0.86) showed a significantly higher area under the curve (AUC) than that of FS unenhanced T1-weighted (0.65, p = 0.010) or first-enhanced phase (0.72, p = 0.049) images. The texture parameters of FS T2-weighted sequences tended to have a higher AUC than CRMP (0.79, p = 0.404). Additionally, the combination strategy exhibited a similar AUC (0.89, p = 0.622) in comparison with the texture parameters of FS T2-weighted sequences. CONCLUSION MRI-TA demonstrated good predictive performance for breast PT pathological grading and could provide surgical planning guidance. Clinical data and routine MRI features were also valuable for grading PTs.
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Affiliation(s)
- Yifei Mao
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhongtang Xiong
- Department of Pathology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Songxin Wu
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China
| | - Zhiqing Huang
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China
| | - Ruoxian Zhang
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China
| | - Yuqin He
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China
| | - Yuling Peng
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yang Ye
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tianfa Dong
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Hui Mai
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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Choudhery S, Gomez-Cardona D, Favazza CP, Hoskin TL, Haddad TC, Goetz MP, Boughey JC. MRI Radiomics for Assessment of Molecular Subtype, Pathological Complete Response, and Residual Cancer Burden in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy. Acad Radiol 2022; 29 Suppl 1:S145-S154. [PMID: 33160859 PMCID: PMC8093323 DOI: 10.1016/j.acra.2020.10.020] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/11/2020] [Accepted: 10/16/2020] [Indexed: 01/03/2023]
Abstract
RATIONALE AND OBJECTIVES There are limited data on pretreatment imaging features that can predict response to neoadjuvant chemotherapy (NAC). To extract volumetric pretreatment MRI radiomics features and assess corresponding associations with breast cancer molecular subtypes, pathological complete response (pCR), and residual cancer burden (RCB) in patients treated with NAC. MATERIALS AND METHODS In this IRB-approved study, clinical and pretreatment MRI data from patients with biopsy-proven breast cancer who received NAC between September 2009 and July 2016 were retrospectively analyzed. Tumors were manually identified and semi-automatically segmented on first postcontrast images. Morphological and three-dimensional textural features were computed, including unfiltered and filtered image data, with spatial scaling factors (SSF) of 2, 4, and 6 mm. Wilcoxon rank-sum tests and area under the receiver operating characteristic curve were used for statistical analysis. RESULTS Two hundred and fifty nine patients with unilateral breast cancer, including 73 (28.2%) HER2+, 112 (43.2%) luminal, and 74 (28.6%) triple negative breast cancers (TNBC), were included. There was a significant difference in the median volume (p = 0.008), median longest axial tumor diameter (p = 0.009), and median longest volumetric diameter (p = 0.01) among tumor subtypes. There was also a significant difference in minimum signal intensity and entropy among the tumor subtypes with SSF = 4 mm (p = 0.009 and p = 0.02 respectively) and SSF = 6 mm (p = 0.007 and p < 0.001 respectively). Additionally, sphericity (p = 0.04) in HER2+ tumors and entropy with SSF = 2, 4, 6 mm (p = 0.004, 0.02, 0.047 respectively) in luminal tumors were significantly associated with pCR. Multiple features demonstrated significant association (p < 0.05) with pCR in TNBC and with RCB in luminal tumors and TNBC, with standard deviation of intensity with SSF = 6 mm achieving the highest AUC (AUC = 0.734) for pCR in TNBC. CONCLUSION MRI radiomics features are associated with different molecular subtypes of breast cancer, pCR, and RCB. These features may be noninvasive imaging biomarkers to identify cancer subtype and predict response to NAC.
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Affiliation(s)
| | | | | | - Tanya L Hoskin
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
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Park HS, Lee KS, Seo BK, Kim ES, Cho KR, Woo OH, Song SE, Lee JY, Cha J. Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients. Cancers (Basel) 2021; 13:cancers13236013. [PMID: 34885124 PMCID: PMC8656976 DOI: 10.3390/cancers13236013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/17/2021] [Accepted: 11/27/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine. Abstract This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.
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Affiliation(s)
- Hyun-Soo Park
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
| | - Kwang-sig Lee
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul 02841, Korea;
| | - Bo-Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
- Correspondence:
| | - Eun-Sil Kim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
| | - Kyu-Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea; (K.-R.C.); (S.-E.S.)
| | - Ok-Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea;
| | - Sung-Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea; (K.-R.C.); (S.-E.S.)
| | - Ji-Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Korea;
| | - Jaehyung Cha
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
- Cheng Hyang NF Co., Ltd., 44-5 Daehak-ro, Jongno-gu, Seoul 03122, Korea
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Lee J, Kim SH, Kang BJ, Lee A, Park WC, Hwang J. Imaging characteristics of young age breast cancer (YABC) focusing on pathologic correlation and disease recurrence. Sci Rep 2021; 11:20205. [PMID: 34642389 PMCID: PMC8511101 DOI: 10.1038/s41598-021-99600-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 09/14/2021] [Indexed: 11/19/2022] Open
Abstract
The purpose of this study is to investigate imaging characteristics of young age breast cancer (YABC) focusing on correlation with pathologic factors and association with disease recurrence. From January 2017 to December 2019, patients under 40 years old who were diagnosed as breast cancer were enrolled in this study. Morphologic analysis of tumor and multiple quantitative parameters were obtained from pre-treatment dynamic contrast enhanced breast magnetic resonance imaging (DCE-MRI). Tumor-stroma ratio (TSR), microvessel density (MVD) and endothelial Notch 1 (EC Notch 1) were investigated for correlation with imaging parameters. In addition, recurrence associated factors were assessed using both clinico-pathologic factors and imaging parameters. A total of 53 patients were enrolled. Several imaging parameters derived from apparent diffusion coefficient (ADC) histogram showed negative correlation with TSR; and there was negative correlation between MVD and Ve in perfusion analysis. There were nine cases of recurrences with median interval of 16 months. Triple negative subtype and low CD34 MVD positivity in Notch 1 hotspots showed significant association with tumor recurrence. Texture parameters reflecting tumor sphericity and homogeneity were also associated with disease recurrence. In conclusion, several quantitative MRI parameters can be used as imaging biomarkers for tumor microenvironment and can predict disease recurrence in YABC.
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Affiliation(s)
- Jeongmin Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
| | - Bong Joo Kang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Ahwon Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Woo-Chan Park
- Division of Breast-Thyroid Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jinwoo Hwang
- Philips Healthcare Korea, Seoul, Republic of Korea
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Ozturk M, Polat AV, Selcuk MB. Whole-lesion ADC histogram analysis versus single-slice ADC measurement for the differentiation of benign and malignant soft tissue tumors. Eur J Radiol 2021; 143:109934. [PMID: 34500411 DOI: 10.1016/j.ejrad.2021.109934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/25/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate and compare the diagnostic performances of whole-lesion apparent diffusion coefficient (ADC) histogram analysis and single-slice ADC measurement in the differentiation of benign and malignant soft tissue tumors. METHODS Fifty-three patients (mean age: 48.5 ± 21.4) with soft tissue tumors (27 benign and 26 malignant) were evaluated with diffusion-weighted MRI. Whole-lesion ADC histogram parameters (mean, median, 10th percentile, 90th percentile, minimum, maximum, range, mean absolute deviation, interquartile range, kurtosis, skewness, root mean squared, variance and inhomogeneity) of the lesions were measured using the whole solid tumor volume region of interest (ROI). In other sessions, five ROIs were manually drawn on the tumor slices, and mean ADC and minimum ADC of the measurements were calculated. Diagnostic accuracies of the two methods were assessed and compared. RESULTS Mean, median, minimum, 10th percentile, 90th percentile, range, root mean squared and inhomogeneity of ADC histogram analysis, and mean ADC and minimum ADC of single-slice ADC measurement were significantly different between malignant and benign lesions (p < 0.001 - p = 0.002). Among the ADC histogram parameters, 10th percentile had the highest diagnostic performance (AUC = 0.825) followed by mean (AUC = 0.792) and median (AUC = 0.789). For the single-slice ADC measurement, the AUC of mean ADC and minimum ADC were 0.842 and 0.786, respectively. Mean ADC of single-slice measurement had a similar diagnostic performance with the 10th percentile, mean, and median of ADC histogram analysis (p = 0.070-1.000). CONCLUSIONS Both whole-lesion ADC histogram analysis and single-slice ADC measurement can differentiate benign and malignant soft tissue tumors with similar diagnostic performances.
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Affiliation(s)
- Mesut Ozturk
- Radiology Clinic, Samsun Gazi State Hospital, Samsun, Turkey; Department of Radiology, Ondokuz Mayis University, Faculty of Medicine, Samsun, Turkey.
| | - Ahmet Veysel Polat
- Department of Radiology, Ondokuz Mayis University, Faculty of Medicine, Samsun, Turkey
| | - Mustafa Bekir Selcuk
- Department of Radiology, Ondokuz Mayis University, Faculty of Medicine, Samsun, Turkey
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Lee EB, Kim SH, Park GE, Lee J, Kang BJ. Risk Stratification of Ductal Carcinoma In Situ and Texture Analysis of Contrast-Enhanced Breast Magnetic Resonance Imaging. J Comput Assist Tomogr 2021; 45:843-848. [PMID: 34347708 DOI: 10.1097/rct.0000000000001205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to investigate whether texture analysis of contrast-enhanced T1 weighted images could predict risk of ductal carcinoma in situ (DCIS). METHODS The study included 185 DCIS lesions that were classified as either low risk or non-low risk using surgical pathology records. All magnetic resonance imaging texture analyses were performed using postprocessing software, and texture-derived parameters were extracted. RESULTS The sphericity, compactness, and spherical disproportion were significantly different in the low-risk and non-low risk groups using the Van Nuys Prognostic Index (mean ± SD, 0.479 ± 0.189 vs 0.414 ± 0.176, 0.161 ± 0.159 vs 0.112 ± 0.134, and 2.569 ± 1.434 vs 2.934 ± 1.374, respectively; P < 0.05). In the univariate analyses, sphericity (odds ratio, 7.091; 95% confidence interval, 1.236-40.666; P = 0.028) and compactness (odds ratio, 9.267; 95% confidence interval, 1.125-76.360; P = 0.039) were significantly associated with a high probability of being low risk according to the Van Nuys Prognostic Index. CONCLUSIONS Whole-lesion texture analysis may be helpful in identifying patients classified as having low-risk DCIS before surgery.
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Affiliation(s)
- Eun Byul Lee
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Kolios C, Sannachi L, Dasgupta A, Suraweera H, DiCenzo D, Stanisz G, Sahgal A, Wright F, Look-Hong N, Curpen B, Sadeghi-Naini A, Trudeau M, Gandhi S, Kolios MC, Czarnota GJ. MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Oncotarget 2021; 12:1354-1365. [PMID: 34262646 PMCID: PMC8274727 DOI: 10.18632/oncotarget.28002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/11/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer. MATERIALS AND METHODS MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance. RESULTS 7 features were significantly (p < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively. CONCLUSIONS Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.
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Affiliation(s)
- Christopher Kolios
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Archya Dasgupta
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Harini Suraweera
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Gregory Stanisz
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Frances Wright
- Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Surgery, University of Toronto, Toronto, Canada
| | - Nicole Look-Hong
- Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Surgery, University of Toronto, Toronto, Canada
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Ali Sadeghi-Naini
- Department of Electrical and Computer Engineering, York University, North York, Canada
| | - Maureen Trudeau
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medicine, University of Toronto, Toronto, Canada
| | - Sonal Gandhi
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medicine, University of Toronto, Toronto, Canada
| | | | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Department of Physics, Ryerson University, Toronto, Canada
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Moyya PD, Asaithambi M. Radiomics- Quantitative Biomarker Analysis for Breast Cancer Diagnosis and Prediction: A Review. Curr Med Imaging 2021; 18:3-17. [PMID: 33655872 DOI: 10.2174/1573405617666210303102526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 01/06/2021] [Accepted: 01/14/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Cancer of the breast has become a global problem for women's health. Though concerns regarding early detection and accurate diagnosis were raised, an effort is required for precision medicine as well as personalized treatment. In the past years, the area of medicinal imaging has seen an unprecedented growth that leads to an advancement of radiomics, which provides countless quantitative biomarkers extracted from modern diagnostic images, including a detailed tumor characterization of breast malignancy. DISCUSSION In this research, we presented the methodology and implementation of radiomics, together with its future trends and challenges by the basis of published papers. Radiomics could distinguish between malignant from benign tumors, predict prognostic factors, molecular subtypes of breast carcinoma, treatment response to neoadjuvant chemotherapy (NAC), and recurrence survival. The incorporation of quantitative knowledge with clinical, histopathological and genomic information will enable physicians to afford customized care of treatment for patients with breast cancer. CONCLUSION Our research was intended to help physicians and radiologists learn fundamental knowledge about radiomics and also to work collaboratively with researchers to explore evidence for further usage in clinical practice.
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Affiliation(s)
- Priscilla Dinkar Moyya
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014. India
| | - Mythili Asaithambi
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014. India
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Ye DM, Wang HT, Yu T. The Application of Radiomics in Breast MRI: A Review. Technol Cancer Res Treat 2020; 19:1533033820916191. [PMID: 32347167 PMCID: PMC7225803 DOI: 10.1177/1533033820916191] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/21/2020] [Accepted: 02/27/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer has been a worldwide burden of women's health. Although concerns have been raised for early diagnosis and timely treatment, the efforts are still needed for precision medicine and individualized treatment. Radiomics is a new technology with immense potential to obtain mineable data to provide rich information about the diagnosis and prognosis of breast cancer. In our study, we introduced the workflow and application of radiomics as well as its outlook and challenges based on published studies. Radiomics has the potential ability to differentiate between malignant and benign breast lesions, predict axillary lymph node status, molecular subtypes of breast cancer, tumor response to chemotherapy, and survival outcomes. Our study aimed to help clinicians and radiologists to know the basic information of radiomics and encourage cooperation with scientists to mine data for better application in clinical practice.
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Affiliation(s)
- Dong-Man Ye
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning Province, People’s Republic of China
| | - Hao-Tian Wang
- Dalian Medical University, The First Clinical College, Dalian, Liaoning Province, People’s Republic of China
| | - Tao Yu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning Province, People’s Republic of China
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Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:2972189. [PMID: 31819738 PMCID: PMC6893252 DOI: 10.1155/2019/2972189] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 10/18/2019] [Accepted: 10/31/2019] [Indexed: 01/14/2023]
Abstract
Objective To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers. Materials and Methods In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype. Results HER2-positive lesions demonstrated significantly higher mean (p=0.034), Perc50 (p=0.046), and Perc90 (p=0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined. Conclusion Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast cancer.
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Contrast-Enhanced MRI Texture Parameters as Potential Prognostic Factors for Primary Central Nervous System Lymphoma Patients Receiving High-Dose Methotrexate-Based Chemotherapy. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:5481491. [PMID: 31777472 PMCID: PMC6875177 DOI: 10.1155/2019/5481491] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 08/26/2019] [Indexed: 02/05/2023]
Abstract
Introduction The purpose of this study was to evaluate the prognostic value of texture features on contrast-enhanced magnetic resonance imaging (MRI) for patients with primary central nervous system lymphoma (PCNSL). Methods In this retrospective study, fifty-two patients diagnosed with PCNSL were enrolled from October 2010 to March 2017. The texture feature of tumor tissue on the histogram-based matrix (histo-) and the grey-level co-occurrence matrix (GLCM) was retrieved by contrast-enhanced T1-weighted imaging before any antitumor treatment. Receiver operating characteristic curve analyses were performed to obtain their optimal cutoff values, based on which we dichotomized patients into subgroups. The Kaplan–Meier analyses were conducted to compare overall survival (OS) of subgroups, and multivariate Cox regression analyses were used to determine if they could be taken as independent prognostic factors. Results Ten texture features were extracted from the MR image, including Energy, Entropy, Kurtosis, Skewness on the histogram-based matrix, and Correlation, Contrast, Dissimilarity, Energy, Entropy, and Homogeneity on the grey-level co-occurrence matrix. Three of them (GLCM-Contrast, GLCM-Dissimilarity, and GLCM-Homogeneity) are shown to be significant in relation to overall survival (OS). The multivariate Cox regression analyses suggest that GLCM-Homogeneity could be taken as independent predictors. Conclusions The texture features of contrast-enhanced magnetic resonance imaging (MRI) could potentially serve as prognostic biomarkers for PCNSL patients.
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Differentiation of Pituitary Adenoma from Rathke Cleft Cyst: Combining MR Image Features with Texture Features. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:6584636. [PMID: 31741657 PMCID: PMC6854938 DOI: 10.1155/2019/6584636] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 06/26/2019] [Indexed: 02/05/2023]
Abstract
Objectives To differentiate pituitary adenoma from Rathke cleft cyst in magnetic resonance (MR) scan by combing MR image features with texture features. Methods A total number of 133 patients were included in this study, 83 with pituitary adenoma and 50 with Rathke cleft cyst. Qualitative MR image features and quantitative texture features were evaluated by using the chi-square tests or Mann–Whitney U test. Binary logistic regression analysis was conducted to investigate their ability as independent predictors. ROC analysis was conducted subsequently on the independent predictors to assess their practical value in discrimination and was used to investigate the association between two types of features. Results Signal intensity on the contrast-enhanced image was found to be the only significantly different MR image feature between the two lesions. Two texture features from the contrast-enhanced images (Histo-Skewness and GLCM-Correlation) were found to be the independent predictors in discrimination, of which AUC values were 0.80 and 0.75, respectively. Besides, the above two texture features (Histo-Skewness and GLCM-Contrast) were suggested to be associated with signal intensity on the contrast-enhanced image. Conclusion Signal intensity on the contrast-enhanced image was the most significant MR image feature in differentiation between pituitary adenoma and Rathke cleft cyst, and texture features also showed promising and practical ability in discrimination. Moreover, two types of features could be coordinated with each other.
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Meyer HJ, Hamerla G, Höhn AK, Surov A. Whole Lesion Histogram Analysis Derived From Morphological MRI Sequences Might be Able to Predict EGFR- and Her2-Expression in Cervical Cancer. Acad Radiol 2019; 26:e208-e215. [PMID: 30318289 DOI: 10.1016/j.acra.2018.09.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 08/31/2018] [Accepted: 09/09/2018] [Indexed: 01/10/2023]
Abstract
RATIONALE AND OBJECTIVES Histogram analysis is an imaging analysis in which a whole tumor can be assessed, and every voxel of a radiological image is issued into a histogram. Thereby, statistically information about tumor can be obtained. The purpose of the study was to analyze possible relationships between histogram parameters derived from conventional MRI sequences and several histopathological features in cervical squamous cell carcinomas. METHODS A total of 18 female patients (age range 32-79 years) with squamous cell cervical carcinoma were retrospectively enrolled into the study. In all cases, pelvic MRI with a clinically protocol was performed. Histogram analysis was performed as a whole lesion measurement, calculating several percentils, minimum, mean, median, mode, maximum, kurtosis, skewness, and entropy. Histopathological parameters included expression of epidermal-growth factor (EGFR), vascular endothelial growth factor, hypoxia-inducible factor 1-alpha, Her2, and Histone 3. Spearman's correlation coefficient was used to analyze associations between investigated parameters. RESULTS Several pre- and postcontrast derived T1-weighted parameters correlated inversely with EGFR expression. For precontrast T1-weighted images, the strongest correlation was found for p90 (ρ = -0.77, p = 0.004). For postcontrast T1-weighted images, the strongest correlation was observed for minimum (ρ = -0.64, p = 0.021). Several parameters derived from T2-weighted images were statistically significant different between Her2-positive and Her2 negative tumors. Skewness had the best p-value ( p = 0.004). CONCLUSIONS Histogram analysis parameters of T1-weighted and T2-weighted images reflect HER2 status and EGFR expression in cervical cancer. Histogram parameters cannot predict cell count, proliferation index, or angiogenesis related histopathological features.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | | | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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20
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Meyer HJ, Renatus K, Höhn AK, Hamerla G, Schopow N, Fakler J, Josten C, Surov A. Texture analysis parameters derived from T1-and T2-weighted magnetic resonance images can reflect Ki67 index in soft tissue sarcoma. Surg Oncol 2019; 30:92-97. [PMID: 31500794 DOI: 10.1016/j.suronc.2019.06.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 04/23/2019] [Accepted: 06/21/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND AND OBJECTIVES Texture analysis derived from morphological magnetic resonance (MR) images might be associated with histopathology in tumors. The present study sought to elucidate possible associations between texture features derived from T1-and T2-weighted images with proliferation index Ki67 in soft tissue sarcomas. METHODS Overall, 29 patients (n = 13, 44.8% female) with a median age of 52 years were included into this retrospective study. Several soft tissue sarcomas were investigated. Texture analysis was performed on pre-contrast T1-weighted and T2-weighted images using the free available Mazda software. RESULTS The best correlation coefficients with Ki67 index were identified for the following parameters: T1-weighted images "45dgr_RLNonUni (p = 0.50, P = 0.006), T2-weighted images "S (4,0)SumAverg" (p = -0.45, P = 0.02). A ROC analysis was performed for Ki67-index with a threshold of 10%. The highest area under the curve (AUC) was found for the parameter "T1_WavEnHL_s-7" with an AUC of 0.90. For the threshold of Ki67 = 20% the highest AUC was identified for the parameter "T2_S (1,1)Entropy" with an AUC of 0.77. CONCLUSION Several texture features derived from T1-and T2-weighted images correlated with proliferation index Ki67 and might be used as valuable novel biomarkers in soft tissue sarcomas.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - Katharina Renatus
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | | | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Nikolas Schopow
- Department of Orthopaedics, Trauma Surgery and Plastic Surgery, University of Leipzig, Leipzig, Germany
| | - Johannes Fakler
- Department of Orthopaedics, Trauma Surgery and Plastic Surgery, University of Leipzig, Leipzig, Germany
| | - Christoph Josten
- Department of Orthopaedics, Trauma Surgery and Plastic Surgery, University of Leipzig, Leipzig, Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M, Turk P, Bera K, Abraham J, Sikov WM, Somlo G, Harris LN, Gilmore H, Plecha D, Varadan V, Madabhushi A. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer. JAMA Netw Open 2019; 2:e192561. [PMID: 31002322 PMCID: PMC6481453 DOI: 10.1001/jamanetworkopen.2019.2561] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
IMPORTANCE There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer. OBJECTIVE To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy. DESIGN, SETTING, AND PARTICIPANTS In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019. MAIN OUTCOMES AND MEASURES Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting. RESULTS In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002). CONCLUSIONS AND RELEVANCE A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.
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Affiliation(s)
- Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Salendra Singh
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Maryam Etesami
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gallagher
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - B. Nicolas Bloch
- Department of Radiology, Boston Medical Center, Boston, Massachusetts
- Department of Radiology, Boston University School of Medicine, Boston, Massachusetts
| | - Manasa Vulchi
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - Paulette Turk
- Department of Diagnostic Radiology, The Cleveland Clinic, Cleveland, Ohio
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jame Abraham
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - William M. Sikov
- Program in Women’s Oncology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - George Somlo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California
| | - Lyndsay N. Harris
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Hannah Gilmore
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Donna Plecha
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Vinay Varadan
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
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Computerized Image Analysis to Differentiate Benign and Malignant Breast Tumors on Magnetic Resonance Diffusion Weighted Image. J Comput Assist Tomogr 2019; 43:93-97. [DOI: 10.1097/rct.0000000000000793] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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23
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He X, Zhang H, Zhang T, Han F, Song B. Predictive models composed by radiomic features extracted from multi-detector computed tomography images for predicting low- and high- grade clear cell renal cell carcinoma: A STARD-compliant article. Medicine (Baltimore) 2019; 98:e13957. [PMID: 30633175 PMCID: PMC6336585 DOI: 10.1097/md.0000000000013957] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
To evaluate the values of conventional image features (CIFs) and radiomic features (RFs) extracted from multi-detector computed tomography (MDCT) images for predicting low- and high-grade clear cell renal cell carcinoma (ccRCC).Two hundred twenty-seven patients with ccRCC were retrospectively recruited. Five hundred seventy features including 14 CIFs and 556 RFs were extracted from MDCT images of each ccRCC. The CIFs were extracted manually and RFs by the free software-MaZda. Least absolute shrinkage and selection operator (Lasso) was applied to shrink the high-dimensional data set and select the features. Five predictive models for predicting low- and high-grade ccRCC were constructed by the selected CIFs and RFs. The 5 models were as follows: model of minimum mean squared error (minMSE) of CIFs (CIF-minMSE), minMSE of cortico-medullary phase (CMP) of kidney (CMP-minMSE), minMSE of parenchyma phase (PP) of kidney (PP-minMSE), the combined model of CIF-minMSE and CMP-minMSE (CIF-CMP-minMSE), and the combined model of CIF-minMSE and PP-minMSE (CIF-PP-minMSE). The Lasso regression equation of each model was constructed, and the predictive values were calculated. The receiver operating characteristic (ROC) curves of predictive values of the 5 models were drawn by SPSS19.0, and the areas under the curves (AUCs) were calculated.According to Lasso regression, 12, 19 and 10 features were respectively selected from the CIFs, RFs of CMP image and that of PP images to construct the 5 predictive models. The models ordered by their AUCs from large to small were CIF-CMP-minMSE (AUC: 0.986), CIF-PP-minMSE (AUC: 0.981), CIF-minMSE (AUC: 0.980), CMP-minMSE (AUC: 0.975), and PP-minMSE (AUC: 0.963). The maximum diameter of the largest axial section of ccRCC had a maximum weight in predicting the grade of ccRCC among all the features, and its cutoff value was 6.15 cm with a sensitivity of 0.901, a specificity of 0.963, and an AUC of 0.975.When combined with CIFs, RFs extracted from MDCT images contributed to the larger AUC of the predictive model, but were less valuable than CIFs when used alone. The CIF-CMP-minMSE was the optimal predictive model. The maximum diameter of the largest axial section of ccRCC had the largest weight in all features.
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Affiliation(s)
- Xiaopeng He
- Department of Radiology, West China Hospital of Sichuan University, Chengdu
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Hanmei Zhang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu
| | - Tong Zhang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu
| | - Fugang Han
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu
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Meyer HJ, Höhn AK, Hamerla G, Surov A. Histogram parameters derived from T2 weighted images are associated with histopathological findings in rectal cancer - a preliminary study. Am J Transl Res 2018; 10:3790-3796. [PMID: 30662629 PMCID: PMC6291705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Accepted: 09/01/2018] [Indexed: 06/09/2023]
Abstract
Histogram analysis can better reflect tumor heterogeneity than conventional imaging analysis. The present study analyzed possible correlations between histogram parameters derived from T2 weighted images and histopathological features in rectal cancer. Seventeen patients with histopathological proven rectal adenocarcinoma were retrospectively acquired with prebioptic 3 T MRI and available histopathological specimens. Histogram analysis was performed using an in-house matlab tool conducting a whole lesion measurement. Histopathology was investigated using Ki67 specimens with calculation of Ki67-index as well as cellularity and nucleic areas and CD31 specimens, with estimation of microvessel density. Several histogram parameters correlated with average nucleic area. Skewness showed a moderate correlation with microvessel density (P = 0.54, P = 0.02). None of the parameters correlated with Ki67-index. Skewness derived from T2 weighted images might be used as a surrogate parameter for average nucleic area and microvessel density. However, none of the parameters were associated with proliferation index.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of LeipzigLeipzig 04103, Germany
| | - Anne Kathrin Höhn
- Department of Pathology, University of LeipzigLeipzig 04103, Germany
| | - Gordian Hamerla
- Department of Diagnostic and Interventional Radiology, University of LeipzigLeipzig 04103, Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of LeipzigLeipzig 04103, Germany
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Meyer HJ, Leifels L, Hamerla G, Höhn AK, Surov A. Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1α, and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma. Mol Imaging Biol 2018; 21:740-746. [DOI: 10.1007/s11307-018-1283-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Meyer HJ, Schob S, Höhn AK, Surov A. MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer - A First Preliminary Study. Transl Oncol 2017; 10:911-916. [PMID: 28987630 PMCID: PMC5645305 DOI: 10.1016/j.tranon.2017.09.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 09/14/2017] [Indexed: 11/23/2022] Open
Abstract
OBJECT Thyroid cancer represents the most frequent malignancy of the endocrine system with an increasing incidence worldwide. Novel imaging techniques are able to further characterize tumors and even predict histopathology features. Texture analysis is an emergent imaging technique to extract extensive data from an radiology images. The present study was therefore conducted to identify possible associations between texture analysis and histopathology parameters in thyroid cancer. METHODS The radiological database was retrospectively reviewed for thyroid carcinoma. Overall, 13 patients (3 females, 23.1%) with a mean age of 61.6 years were identified. The MaZda program was used for texture analysis. The T1-precontrast and T2-weighted images were analyzed and overall 279 texture feature for each sequence was investigated. For every patient cell count, Ki67-index and p53 count were investigated. RESULTS Several significant correlations between texture features and histopathology were identified. Regarding T1-weighted images, S(0;1)Sum Averg correlated the most with cell count (r=0.82). An inverse correlations with S(5;0)AngScMom, S(5;0)DifVarnc S(5;0), DiffEntrp and GrNonZeros (r=-0.69, -0.66, -0.69 and -0.63, respectively) was also identified. For T2-weighted images, Variance with r=0.63 was the highest coefficient, WavEnLL_S3 correlated inversely with cell count (r=-0.57). WavEnLL_S2 derived from T1-weighted images was the highest coefficient r=-0.80, S(0;5)SumVarnc was positively with r=0.74. Regarding T2-weighted images WavEnHL_s-1 was inverse correlated with Ki67 index (r=-0.77). S(1;0)Correlat was with r=0.75 the best correlation with Ki67 index. For T1-weighed images S(5;0)SumofSqs was the best with r=0.65 with p53 count. For T2-weighted images S(1;-1)SumEntrp was the inverse correlation with r=-0.72, whereas S(0;4)AngScMom correlated positively with r=0.63. CONCLUSIONS MRI texture analysis derived from conventional sequences reflects histopathology features in thyroid cancer. This technique might be a novel noninvasive modality to further characterize thyroid cancer in clinical oncology.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - Stefan Schob
- Department of Neuroradiology, University of Leipzig, Leipzig, Germany
| | | | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Collagen Complexity Spatially Defines Microregions of Total Tissue Pressure in Pancreatic Cancer. Sci Rep 2017; 7:10093. [PMID: 28855644 PMCID: PMC5577321 DOI: 10.1038/s41598-017-10671-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 08/11/2017] [Indexed: 12/18/2022] Open
Abstract
The poor efficacy of systemic cancer therapeutics in pancreatic ductal adenocarcinoma (PDAC) is partly attributed to deposition of collagen and hyaluronan, leading to interstitial hypertension collapsing blood and lymphatic vessels, limiting drug delivery. The intrinsic micro-regional interactions between hyaluronic acid (HA), collagen and the spatial origins of mechanical stresses that close off blood vessels was investigated here. Multiple localized pressure measurements were analyzed with spatially-matched histochemical images of HA, collagen and vessel perfusion. HA is known to swell, fitting a linear elastic model with total tissue pressure (TTP) increasing above interstitial fluid pressure (IFP) directly with collagen content. However, local TTP appears to originate from collagen area fraction, as well as increased its entropy and fractal dimension, and morphologically appears to be maximized when HA regions are encapsulated by collagen. TTP was inversely correlated with vascular patency and verteporfin uptake, suggesting interstitial hypertension results in vascular compression and decreased molecular delivery in PDAC. Collagenase injection led to acute decreases in total tissue pressure and increased drug perfusion. Large microscopic variations in collagen distributions within PDAC leads to microregional TPP values that vary on the hundred micron distance scale, causing micro-heterogeneous limitations in molecular perfusion, and narrows viable treatment regimes for systemically delivered therapeutics.
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28
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Kim HS, Kim JH, Yoon YC, Choe BK. Tumor spatial heterogeneity in myxoid-containing soft tissue using texture analysis of diffusion-weighted MRI. PLoS One 2017; 12:e0181339. [PMID: 28708850 PMCID: PMC5510859 DOI: 10.1371/journal.pone.0181339] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 06/03/2017] [Indexed: 01/01/2023] Open
Abstract
The objective of this study was to examine the tumor spatial heterogeneity in myxoid-containing soft-tissue tumors (STTs) using texture analysis of diffusion-weighted imaging (DWI). A total of 40 patients with myxoid-containing STTs (23 benign and 17 malignant) were included in this study. The region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map. For texture analysis, the global (mean, standard deviation, skewness, and kurtosis), regional (intensity variability and size-zone variability), and local features (energy, entropy, correlation, contrast, homogeneity, variance, and maximum probability) were extracted from the ADC map. Student’s t-test was used to test the difference between group means. Analysis of covariance (ANCOVA) was performed with adjustments for age, sex, and tumor volume. The receiver operating characteristic (ROC) analysis was performed to compare diagnostic performances. Malignant myxoid-containing STTs had significantly higher kurtosis (P = 0.040), energy (P = 0.034), correlation (P<0.001), and homogeneity (P = 0.003), but significantly lower contrast (P<0.001) and variance (P = 0.001) compared with benign myxoid-containing STTs. Contrast showed the highest area under the curve (AUC = 0.923, P<0.001), sensitivity (94.12%), and specificity (86.96%). Our results reveal the potential utility of texture analysis of ADC maps for differentiating benign and malignant myxoid-containing STTs.
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Affiliation(s)
- Hyun Su Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #50 Irwon-dong, Gangnam-gu, Seoul, Republic of Korea
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #50 Irwon-dong, Gangnam-gu, Seoul, Republic of Korea
- * E-mail:
| | - Young Cheol Yoon
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #50 Irwon-dong, Gangnam-gu, Seoul, Republic of Korea
| | - Bong Keun Choe
- Department of Preventive Medicine, Medical College, Kyung Hee University, Seoul, Republic of Korea
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Verma RK, Wiest R, Locher C, Heldner MR, Schucht P, Raabe A, Gralla J, Kamm CP, Slotboom J, Kellner‐Weldon F. Differentiating enhancing multiple sclerosis lesions, glioblastoma, and lymphoma with dynamic texture parameters analysis (
DTPA
): A feasibility study. Med Phys 2017; 44:4000-4008. [DOI: 10.1002/mp.12356] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 03/13/2017] [Accepted: 05/13/2017] [Indexed: 12/11/2022] Open
Affiliation(s)
- Rajeev Kumar Verma
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
- Institute of Radiology and Neuroradiology Tiefenau Hospital Bern 3004 Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | - Cäcilia Locher
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | | | - Phillip Schucht
- Department of Neurosurgery Inselspital University of Bern Bern 3010 Switzerland
| | - Andreas Raabe
- Department of Neurosurgery Inselspital University of Bern Bern 3010 Switzerland
| | - Jan Gralla
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | | | - Johannes Slotboom
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
| | - Frauke Kellner‐Weldon
- Support Center for Advanced Neuroimaging University Institute of Diagnostic and Interventional Neuroradiology Inselspital University of Bern Bern 3010 Switzerland
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Liu S, Zheng H, Zhang Y, Chen L, Guan W, Guan Y, Ge Y, He J, Zhou Z. Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness. J Magn Reson Imaging 2017; 47:168-175. [PMID: 28471511 DOI: 10.1002/jmri.25752] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 04/13/2017] [Indexed: 12/19/2022] Open
Abstract
PURPOSE To explore the role of whole-volume apparent diffusion coefficient (ADC)-based entropy parameters in the preoperative assessment of gastric cancer's aggressiveness. MATERIALS AND METHODS In all, 64 patients with gastric cancers who underwent 3.0T magnetic resonance imaging (MRI) were retrospectively included. Regions of interest were drawn manually using in-house software, around gastric cancer lesions on each slice of the diffusion-weighted images and ADC maps. Entropy-related parameters based on ADC maps were calculated automatically: (1) first-order entropy; (2-5) second-order entropies, including entropy(H)0 , entropy(H)45 , entropy(H)90 , and entropy(H)135 ; (6) entropy(H)mean ; and (7) entropy(H)range . Correlations between entropy-related parameters and pathological characteristics were analyzed with the Spearman correlation test. The parameters were compared among different pathological characteristics with independent-samples Kruskal-Wallis or Mann-Whitney U-test. Additionally, diagnostic performances of parameters in differentiating different pathological characteristics were analyzed by receiver operating characteristic (ROC) curve analysis. RESULTS All the entropy-related parameters significantly correlated with T, N, and overall stages, especially the first-order entropy (r = 0.588, 0.585, and 0.677, respectively, all P < 0.05). All the entropy-related parameters showed significant differences in gastric cancers at different T, N, and overall stages, as well as at different status of vascular invasion (P < 0.001-0.027). And four parameters, including entropy, entropy(H)0 , entropy(H)45 , and entropy(H)90 , showed significant differences between gastric cancers with and without perineural invasion (P 0.006-0.040). CONCLUSION Entropy-related parameters derived from whole-volume ADC texture analysis could help assess the aggressiveness of gastric cancers via analyzing intratumoral heterogeneity quantitatively, especially the first-order entropy. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:168-175.
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Affiliation(s)
- Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Huanhuan Zheng
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Yujuan Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Ling Chen
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Wenxian Guan
- Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Yue Guan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, P.R. China
| | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, P.R. China
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, P.R. China
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Chou SHS, Gombos EC, Chikarmane SA, Giess CS, Jayender J. Computer-aided heterogeneity analysis in breast MR imaging assessment of ductal carcinoma in situ: Correlating histologic grade and receptor status. J Magn Reson Imaging 2017; 46:1748-1759. [PMID: 28371110 DOI: 10.1002/jmri.25712] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 03/06/2017] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To identify breast MR imaging biomarkers to predict histologic grade and receptor status of ductal carcinoma in situ (DCIS). MATERIALS AND METHODS Informed consent was waived in this Health Insurance Portability and Accountability Act-compliant Institutional Review Board-approved study. Case inclusion was conducted from 7332 consecutive breast MR studies from January 1, 2009, to December 31, 2012. Excluding studies with benign diagnoses, studies without visible abnormal enhancement, and pathology containing invasive disease yielded 55 MR-imaged pathology-proven DCIS seen on 54 studies. Twenty-eight studies (52%) were performed at 1.5 Tesla (T); 26 (48%) at 3T. Regions-of-interest representing DCIS were segmented for precontrast, first and fourth postcontrast, and subtracted first and fourth postcontrast images on the open-source three-dimensional (3D) Slicer software. Fifty-seven metrics of each DCIS were obtained, including distribution statistics, shape, morphology, Renyi dimensions, geometrical measure, and texture, using the 3D Slicer HeterogeneityCAD module. Statistical correlation of heterogeneity metrics with DCIS grade and receptor status was performed using univariate Mann-Whitney test. RESULTS Twenty-four of the 55 DCIS (44%) were high nuclear grade (HNG); 44 (80%) were estrogen receptor (ER) positive. Human epidermal growth factor receptor-2 (HER2) was amplified in 10/55 (18%), nonamplified in 34/55 (62%), unknown/equivocal in 8/55 (15%). Surface area-to-volume ratio showed significant difference (P < 0.05) between HNG and non-HNG DCIS. No metric differentiated ER status (0.113 < p ≤ 1.000). Seventeen metrics showed significant differences between HER2-positive and HER2-negative DCIS (0.016 < P < 0.050). CONCLUSION Quantitative heterogeneity analysis of DCIS suggests the presence of MR imaging biomarkers in classifying DCIS grade and HER2 status. Validation with larger samples and prospective studies is needed to translate these results into clinical applications. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1748-1759.
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Affiliation(s)
- Shinn-Huey S Chou
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Eva C Gombos
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Sona A Chikarmane
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Catherine S Giess
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jagadeesan Jayender
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
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Affiliation(s)
- Brandon Faubert
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, Texas 75390-8502
| | - Ralph J. DeBerardinis
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, Texas 75390-8502
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas 75390-8502
- McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, Texas 75390-8502
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33
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Song JL, Chen C, Yuan JP, Sun SR. Progress in the clinical detection of heterogeneity in breast cancer. Cancer Med 2016; 5:3475-3488. [PMID: 27774765 PMCID: PMC5224851 DOI: 10.1002/cam4.943] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 09/22/2016] [Accepted: 09/23/2016] [Indexed: 12/18/2022] Open
Abstract
Breast cancer is currently the most common form of cancer and the second‐leading cause of death from cancer in women. Though considerable progress has been made in the treatment of breast cancer, the heterogeneity of tumors (both inter‐ and intratumor) remains a considerable diagnostic and prognostic challenge. From clinical observation to genetic mutations, the history of understanding the heterogeneity of breast cancer is lengthy and detailed. Effectively detecting heterogeneity in breast cancer is important during treatment. Various methods of depicting this heterogeneity are now available and include genetic, pathologic, and imaging analysis. These methods allow characterization of the heterogeneity of breast cancer on a genetic level, providing greater insight during the process of establishing an effective therapeutic plan. This study reviews how the understanding of tumor heterogeneity in breast cancer evolved, and further summarizes recent advances in the detection and monitoring of this heterogeneity in patients with breast cancer.
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Affiliation(s)
- Jun-Long Song
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Chuang Chen
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Jing-Ping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
| | - Sheng-Rong Sun
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China
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