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Hatamikia S, George G, Schwarzhans F, Mahbod A, Woitek R. Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation? Comput Struct Biotechnol J 2024; 23:52-63. [PMID: 38125296 PMCID: PMC10730996 DOI: 10.1016/j.csbj.2023.11.016] [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: 05/14/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.
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Affiliation(s)
- Sepideh Hatamikia
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
- Austrian Center for Medical Innovation and Technology (ACMIT), Viktor Kaplan-Straße 2/1, Wiener Neustadt 2700, Austria
| | - Geevarghese George
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Florian Schwarzhans
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Amirreza Mahbod
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Ramona Woitek
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
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Oliveira C, Oliveira F, Constantino C, Alves C, Brito MJ, Cardoso F, Costa DC. Baseline [ 18F]FDG PET/CT and MRI first-order breast tumor features do not improve pathological complete response prediction to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06815-6. [PMID: 38922396 DOI: 10.1007/s00259-024-06815-6] [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: 03/12/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024]
Abstract
PURPOSE To verify the ability of pretreatment [18F]FDG PET/CT and T1-weighed dynamic contrast-enhanced MRI to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. METHODS This retrospective study includes patients with BC of no special type submitted to baseline [18F]FDG PET/CT, NAC and surgery. [18F]FDG PET-based features reflecting intensity and heterogeneity of tracer uptake were extracted from the primary BC and suspicious axillary lymph nodes (ALN), for comparative analysis related to NAC response (pCR vs. non-pCR). Multivariate logistic regression was performed for response prediction combining the breast tumor-extracted PET-based features and clinicopathological features. A subanalysis was performed in a patients' subsample by adding breast tumor-extracted first-order MRI-based features to the multivariate logistic regression. RESULTS A total of 170 tumors from 168 patients were included. pCR was observed in 60/170 tumors (20/107 luminal B-like, 25/45 triple-negative and 15/18 HER2-enriched surrogate molecular subtypes). Higher intensity and higher heterogeneity of [18F]FDG uptake in the primary BC were associated with NAC response in HER2-negative tumors (immunohistochemistry score 0, 1 + or 2 + non-amplified by in situ hybridization). Also, higher intensity of tracer uptake was observed in ALN in the pCR group among HER2-negative tumors. No [18F]FDG PET-based features were associated with pCR in the other subgroup analyses. A subsample of 103 tumors was also submitted to extraction of MRI-based features. When combined with clinicopathological features, neither [18F]FDG PET nor MRI-based features had additional value for pCR prediction. The only significant predictors were estrogen receptor status, HER2 expression and grade. CONCLUSION Pretreatment [18F]FDG PET-based features from primary BC and ALN are not associated with response to NAC, except in HER2-negative tumors. As compared with pathological features, no breast tumor-extracted PET or MRI-based feature improved response prediction.
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Affiliation(s)
- Carla Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal.
| | - Francisco Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Cláudia Constantino
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Celeste Alves
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Maria José Brito
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
- Pathology Department, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Fátima Cardoso
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Durval C Costa
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
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Xie T, Gong J, Zhao Q, Wu C, Wu S, Peng W, Gu Y. Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer. BMC Med Imaging 2024; 24:136. [PMID: 38844842 PMCID: PMC11155097 DOI: 10.1186/s12880-024-01311-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/27/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, USA
| | - Siyu Wu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Duwe G, Mercier D, Wiesmann C, Kauth V, Moench K, Junker M, Neumann CCM, Haferkamp A, Dengel A, Höfner T. Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology. Cancer Med 2024; 13:e7398. [PMID: 38923826 PMCID: PMC11196383 DOI: 10.1002/cam4.7398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/31/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.
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Affiliation(s)
- Gregor Duwe
- Department of Urology and Pediatric UrologyUniversity Medical Center, Johannes Gutenberg UniversityMainzGermany
| | - Dominique Mercier
- Research Unit Smart Data and Knowledge ServicesGerman Research Center for Artificial IntelligenceKaiserslauternGermany
| | - Crispin Wiesmann
- Department of Urology and Pediatric UrologyUniversity Medical Center, Johannes Gutenberg UniversityMainzGermany
| | - Verena Kauth
- Department of Urology and Pediatric UrologyUniversity Medical Center, Johannes Gutenberg UniversityMainzGermany
| | - Kerstin Moench
- Department of Urology and Pediatric UrologyUniversity Medical Center, Johannes Gutenberg UniversityMainzGermany
| | - Markus Junker
- Research Unit Smart Data and Knowledge ServicesGerman Research Center for Artificial IntelligenceKaiserslauternGermany
| | - Christopher C. M. Neumann
- Department of Hematology, Oncology and Tumor ImmunologyCharité‐Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt‐Universität zu BerlinBerlinGermany
| | - Axel Haferkamp
- Department of Urology and Pediatric UrologyUniversity Medical Center, Johannes Gutenberg UniversityMainzGermany
| | - Andreas Dengel
- Research Unit Smart Data and Knowledge ServicesGerman Research Center for Artificial IntelligenceKaiserslauternGermany
| | - Thomas Höfner
- Department of Urology and Pediatric UrologyUniversity Medical Center, Johannes Gutenberg UniversityMainzGermany
- Department of Urology, Ordensklinikum Linz ElisabethinenLinzAustria
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Tabnak P, HajiEsmailPoor Z, Baradaran B, Pashazadeh F, Aghebati Maleki L. MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis. Acad Radiol 2024; 31:763-787. [PMID: 37925343 DOI: 10.1016/j.acra.2023.10.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/01/2023] [Accepted: 10/04/2023] [Indexed: 11/06/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer. MATERIALS AND METHODS A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis. RESULTS 31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results. CONCLUSION This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.
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Affiliation(s)
- Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Zanyar HajiEsmailPoor
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Behzad Baradaran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Fariba Pashazadeh
- Research Center for Evidence-Based Medicine, Iranian Evidence-Based Medicine (EBM) Centre: A Joanna Briggs Institute (JBI) Centre of Excellence, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (F.P.)
| | - Leili Aghebati Maleki
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.).
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Yu Y, Wang Z, Wang Q, Su X, Li Z, Wang R, Guo T, Gao W, Wang H, Zhang B. Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients. Front Oncol 2024; 13:1249339. [PMID: 38357424 PMCID: PMC10865896 DOI: 10.3389/fonc.2023.1249339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/02/2023] [Indexed: 02/16/2024] Open
Abstract
Purpose To establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients. Method MRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves. Results The AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram. Conclusion We have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.
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Affiliation(s)
- Yimiao Yu
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhibo Wang
- Department of Gastroenterological Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qi Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaohui Su
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhenghao Li
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruifeng Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianhui Guo
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen Gao
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haiji Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Biyuan Zhang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Kong J, Zhang D. Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis. Front Neurol 2024; 14:1335851. [PMID: 38229595 PMCID: PMC10789857 DOI: 10.3389/fneur.2023.1335851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024] Open
Abstract
Background Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients. Methods We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients. Results Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84-0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74-0.86) for predicting reperfusion in AIS. Conclusion Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.
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Affiliation(s)
- Jinfen Kong
- Department of Radiology, Yuhuan Second People's Hospital, Yuhuan, Taizhou, Zhejiang, China
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Kazerouni AS, Peterson LM, Jenkins I, Novakova-Jiresova A, Linden HM, Gralow JR, Hockenbery DM, Mankoff DA, Porter PL, Partridge SC, Specht JM. Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer. Breast Cancer Res 2023; 25:138. [PMID: 37946201 PMCID: PMC10636950 DOI: 10.1186/s13058-023-01722-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/26/2023] [Indexed: 11/12/2023] Open
Abstract
PURPOSE To investigate combined MRI and 18F-FDG PET for assessing breast tumor metabolism/perfusion mismatch and predicting pathological response and recurrence-free survival (RFS) in women treated for breast cancer. METHODS Patients undergoing neoadjuvant chemotherapy (NAC) for locally-advanced breast cancer were imaged at three timepoints (pre, mid, and post-NAC), prior to surgery. Imaging included diffusion-weighted and dynamic contrast-enhanced (DCE-) MRI and quantitative 18F-FDG PET. Tumor imaging measures included apparent diffusion coefficient, peak percent enhancement (PE), peak signal enhancement ratio (SER), functional tumor volume, and washout volume on MRI and standardized uptake value (SUVmax), glucose delivery (K1) and FDG metabolic rate (MRFDG) on PET, with percentage changes from baseline calculated at mid- and post-NAC. Associations of imaging measures with pathological response (residual cancer burden [RCB] 0/I vs. II/III) and RFS were evaluated. RESULTS Thirty-five patients with stage II/III invasive breast cancer were enrolled in the prospective study (median age: 43, range: 31-66 years, RCB 0/I: N = 11/35, 31%). Baseline imaging metrics were not significantly associated with pathologic response or RFS (p > 0.05). Greater mid-treatment decreases in peak PE, along with greater post-treatment decreases in several DCE-MRI and 18F-FDG PET measures were associated with RCB 0/I after NAC (p < 0.05). Additionally, greater mid- and post-treatment decreases in DCE-MRI (peak SER, washout volume) and 18F-FDG PET (K1) were predictive of prolonged RFS. Mid-treatment decreases in metabolism/perfusion ratios (MRFDG/peak PE, MRFDG/peak SER) were associated with improved RFS. CONCLUSION Mid-treatment changes in both PET and MRI measures were predictive of RCB status and RFS following NAC. Specifically, our results indicate a complementary relationship between DCE-MRI and 18F-FDG PET metrics and potential value of metabolism/perfusion mismatch as a marker of patient outcome.
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Affiliation(s)
- Anum S Kazerouni
- Department of Radiology, University of Washington/Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Lanell M Peterson
- Division of Hematology and Oncology, University of Washington/Fred Hutchinson Cancer Center, 1144 Eastlake (Mail Stop LG-500), Seattle, WA, 98109-1023, USA
| | | | | | - Hannah M Linden
- Division of Hematology and Oncology, University of Washington/Fred Hutchinson Cancer Center, 1144 Eastlake (Mail Stop LG-500), Seattle, WA, 98109-1023, USA
| | - Julie R Gralow
- American Society of Clinical Oncology, Alexandria, VA, USA
| | | | - David A Mankoff
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Savannah C Partridge
- Department of Radiology, University of Washington/Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jennifer M Specht
- Division of Hematology and Oncology, University of Washington/Fred Hutchinson Cancer Center, 1144 Eastlake (Mail Stop LG-500), Seattle, WA, 98109-1023, USA.
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10
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Malhaire C, Selhane F, Saint-Martin MJ, Cockenpot V, Akl P, Laas E, Bellesoeur A, Ala Eddine C, Bereby-Kahane M, Manceau J, Sebbag-Sfez D, Pierga JY, Reyal F, Vincent-Salomon A, Brisse H, Frouin F. Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy. Eur Radiol 2023; 33:8142-8154. [PMID: 37318605 DOI: 10.1007/s00330-023-09797-5] [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: 12/15/2022] [Revised: 04/14/2023] [Accepted: 05/13/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVES To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. RESULTS Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction. CONCLUSION Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. CLINICAL RELEVANCE STATEMENT A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes. KEY POINTS • Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis. • Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC. • Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.
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Affiliation(s)
- Caroline Malhaire
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France.
- Institut Curie, Research Center, U1288-LITO, Inserm, Paris-Saclay University, 91401, Orsay, France.
| | - Fatine Selhane
- Gustave Roussy, Department of Imaging, Paris-Saclay University, 94805, Villejuif, France
| | | | - Vincent Cockenpot
- Pathology Unit, Centre Léon Bérard, 28 Rue Laennec, 69008, Lyon, France
| | - Pia Akl
- Women Imaging Unit, HCL, Radiologie du Groupement Hospitalier Est, 3 Quai Des Célestins, 69002, Lyon, France
| | - Enora Laas
- Department of Surgical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Audrey Bellesoeur
- Department of Medical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Catherine Ala Eddine
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Melodie Bereby-Kahane
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Julie Manceau
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Delphine Sebbag-Sfez
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Jean-Yves Pierga
- Department of Medical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Fabien Reyal
- Department of Surgical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | | | - Herve Brisse
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Frederique Frouin
- Institut Curie, Research Center, U1288-LITO, Inserm, Paris-Saclay University, 91401, Orsay, France
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11
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Zhang J, Cui Z, Shi Z, Jiang Y, Zhang Z, Dai X, Yang Z, Gu Y, Zhou L, Han C, Huang X, Ke C, Li S, Xu Z, Gao F, Zhou L, Wang R, Liu J, Zhang J, Ding Z, Sun K, Li Z, Liu Z, Shen D. A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework. PATTERNS (NEW YORK, N.Y.) 2023; 4:100826. [PMID: 37720328 PMCID: PMC10499873 DOI: 10.1016/j.patter.2023.100826] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/25/2023] [Accepted: 07/21/2023] [Indexed: 09/19/2023]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Zhiming Cui
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Yingjia Jiang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Hunan 410011, China
| | - Zhiliang Zhang
- School of Medical Imaging, Hangzhou Medical College, Zhejiang 310059, China
| | - Xiaoting Dai
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhenlu Yang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guizhou 550002, China
| | - Yuning Gu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Lei Zhou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Xiaomei Huang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Chenglu Ke
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Suyun Li
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Fei Gao
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Luping Zhou
- School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guizhou 550002, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Hunan 410011, China
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou 310003, China
| | - Kun Sun
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China
- Shanghai Clinical Research and Trial Center, Shanghai 200052, China
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12
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Calimano-Ramirez LF, Virarkar MK, Hernandez M, Ozdemir S, Kumar S, Gopireddy DR, Lall C, Balaji KC, Mete M, Gumus KZ. MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Abdom Radiol (NY) 2023; 48:2379-2400. [PMID: 37142824 DOI: 10.1007/s00261-023-03924-y] [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: 01/13/2023] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature. METHODS We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores. RESULTS We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science. CONCLUSION Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.
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Affiliation(s)
- Luis F Calimano-Ramirez
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Mayur K Virarkar
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Mauricio Hernandez
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Savas Ozdemir
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Sindhu Kumar
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Dheeraj R Gopireddy
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA
| | - K C Balaji
- Department of Urology, University of Florida College of Medicine, Jacksonville, FL, 32209, USA
| | - Mutlu Mete
- Department of Computer Science and Information System, Texas A&M University-Commerce, Commerce, TX, 75428, USA
| | - Kazim Z Gumus
- Department of Radiology, University of Florida College of Medicine Jacksonville, Jacksonville, FL, 32209, USA.
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13
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Oliveira C, Oliveira F, Vaz SC, Marques HP, Cardoso F. Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer. Br J Radiol 2023; 96:20220655. [PMID: 36867773 DOI: 10.1259/bjr.20220655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Complete pathological response to neoadjuvant systemic treatment (NAST) in some subtypes of breast cancer (BC) has been used as a surrogate of long-term outcome. The possibility of predicting BC pathological response to NAST based on the baseline 18F-Fluorodeoxyglucose positron emission tomography (FDG PET), without the need of an interim study, is a focus of recent discussion. This review summarises the characteristics and results of the available studies regarding the potential impact of heterogeneity features of the primary tumour burden on baseline FDG PET in predicting pathological response to NAST in BC patients. Literature search was conducted on PubMed database and relevant data from each selected study were collected. A total of 13 studies were eligible for inclusion, all of them published over the last 5 years. Eight out of 13 analysed studies indicated an association between FDG PET-based tumour uptake heterogeneity features and prediction of response to NAST. When features associated with predicting response to NAST were derived, these varied between studies. Therefore, definitive reproducible findings across series were difficult to establish. This lack of consensus may reflect the heterogeneity and low number of included series. The clinical relevance of this topic justifies further investigation about the predictive role of baseline FDG PET.
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Affiliation(s)
- Carla Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | - Francisco Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | - Sofia C Vaz
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | | | - Fátima Cardoso
- Breast Unit, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
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14
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Spadarella G, Stanzione A, Akinci D'Antonoli T, Andreychenko A, Fanni SC, Ugga L, Kotter E, Cuocolo R. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol 2023; 33:1884-1894. [PMID: 36282312 PMCID: PMC9935718 DOI: 10.1007/s00330-022-09187-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features. METHODS The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features. RESULTS The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other). CONCLUSIONS Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space. KEY POINTS • Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.
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Affiliation(s)
- Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | | | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Elmar Kotter
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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15
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MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13050857. [PMID: 36900001 PMCID: PMC10000411 DOI: 10.3390/diagnostics13050857] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.
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Mammography-based radiomics analysis and imaging features for predicting the malignant risk of phyllodes tumours of the breast. Clin Radiol 2023; 78:e386-e392. [PMID: 36868973 DOI: 10.1016/j.crad.2023.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/21/2023]
Abstract
AIM To determine whether the mammography (MG)-based radiomics analysis and MG/ultrasound (US) imaging features could predict the malignant risk of phyllodes tumours (PTs) of the breast. MATERIALS AND METHODS Seventy-five patients with PTs were included retrospectively (39 with benign PTs, 36 with borderline/malignant PTs) and divided into thetraining (n=52) and validation groups (n=23). The clinical information, MG and US imaging characteristics, and histogram features were extracted from craniocaudal (CC) and mediolateral oblique (MLO) images. The lesion region of interest (ROI) and perilesional ROI were delineated. Multivariate logistic regression analysis was performed to determine the malignant factors of PTs. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC), sensitivity, and specificity were calculated. RESULTS There was no significant difference found in the clinical or MG/US features between benign and borderline/malignant PTs. In the lesion ROI, variance in the CC view and mean and variance in the MLO view were independent predictors. The AUC was 0.942, sensitivity and specificity were 96.3% and 92%, respectively, in the training group. In the validation group, the AUC was 0.879, the sensitivity was 91.7%, and the specificity was 81.8%. In the perilesional ROI, the AUCs were 0.904 and 0.939, sensitivities were 88.9% and 91.7%, and the specificities were 92% and 90.9% in the training and validation groups, respectively. CONCLUSIONS MG-based radiomic features could predict the risk of malignancy of patients with PTs and may be used as a potential tool to differentiate benign and borderline/malignant PTs.
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17
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Early Assessment of Neoadjuvant Chemotherapy Response Using Multiparametric Magnetic Resonance Imaging in Luminal B-like Subtype of Breast Cancer Patients: A Single-Center Prospective Study. Diagnostics (Basel) 2023; 13:diagnostics13040694. [PMID: 36832182 PMCID: PMC9955433 DOI: 10.3390/diagnostics13040694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/05/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
This study aimed to evaluate the performance of multiparametric breast magnetic resonance imaging (mpMRI) for predicting response to neoadjuvant chemotherapy (NAC) in patients with luminal B subtype breast cancer. The prospective study included thirty-five patients treated with NAC for both early and locally advanced breast cancer of the luminal B subtype at the University Hospital Centre Zagreb between January 2015 and December 2018. All patients underwent breast mpMRI before and after two cycles of NAC. Evaluation of mpMRI examinations included analysis of both morphological (shape, margins, and pattern of enhancement) and kinetic characteristics (initial signal increase and post-initial behavior of the time-signal intensity curve), which were additionally interpreted with a Göttingen score (GS). Histopathological analysis of surgical specimens included grading the tumor response based on the residual cancer burden (RCB) grading system and revealed 29 NAC responders (RCB-0 (pCR), I, II) and 6 NAC non-responders (RCB-III). Changes in GS were compared with RCB classes. A lack of GS decrease after the second cycle of NAC is associated with RCB class and non-responders to NAC.
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18
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Ponsiglione A, Stanzione A, Spadarella G, Baran A, Cappellini LA, Lipman KG, Van Ooijen P, Cuocolo R. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative. Eur Radiol 2023; 33:2239-2247. [PMID: 36303093 PMCID: PMC9935717 DOI: 10.1007/s00330-022-09180-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/26/2022] [Accepted: 09/18/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. METHODS Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. RESULTS From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. CONCLUSIONS The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making. KEY POINTS • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, -0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.
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Affiliation(s)
- Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | - Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Agah Baran
- Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | | | - Kevin Groot Lipman
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Peter Van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health, University Medical Center Groningen, Groningen, the Netherlands
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI. Eur Radiol 2023; 33:959-969. [PMID: 36074262 PMCID: PMC9889463 DOI: 10.1007/s00330-022-09113-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/09/2022] [Accepted: 08/14/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To develop a visual ensemble selection of deep convolutional neural networks (CNN) for 3D segmentation of breast tumors using T1-weighted dynamic contrast-enhanced (T1-DCE) MRI. METHODS Multi-center 3D T1-DCE MRI (n = 141) were acquired for a cohort of patients diagnosed with locally advanced or aggressive breast cancer. Tumor lesions of 111 scans were equally divided between two radiologists and segmented for training. The additional 30 scans were segmented independently by both radiologists for testing. Three 3D U-Net models were trained using either post-contrast images or a combination of post-contrast and subtraction images fused at either the image or the feature level. Segmentation accuracy was evaluated quantitatively using the Dice similarity coefficient (DSC) and the Hausdorff distance (HD95) and scored qualitatively by a radiologist as excellent, useful, helpful, or unacceptable. Based on this score, a visual ensemble approach selecting the best segmentation among these three models was proposed. RESULTS The mean and standard deviation of DSC and HD95 between the two radiologists were equal to 77.8 ± 10.0% and 5.2 ± 5.9 mm. Using the visual ensemble selection, a DSC and HD95 equal to 78.1 ± 16.2% and 14.1 ± 40.8 mm was reached. The qualitative assessment was excellent (resp. excellent or useful) in 50% (resp. 77%). CONCLUSION Using subtraction images in addition to post-contrast images provided complementary information for 3D segmentation of breast lesions by CNN. A visual ensemble selection allowing the radiologist to select the most optimal segmentation obtained by the three 3D U-Net models achieved comparable results to inter-radiologist agreement, yielding 77% segmented volumes considered excellent or useful. KEY POINTS • Deep convolutional neural networks were developed using T1-weighted post-contrast and subtraction MRI to perform automated 3D segmentation of breast tumors. • A visual ensemble selection allowing the radiologist to choose the best segmentation among the three 3D U-Net models outperformed each of the three models. • The visual ensemble selection provided clinically useful segmentations in 77% of cases, potentially allowing for a valuable reduction of the manual 3D segmentation workload for the radiologist and greatly facilitating quantitative studies on non-invasive biomarker in breast MRI.
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Caballo M, Sanderink WBG, Han L, Gao Y, Athanasiou A, Mann RM. Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI. J Magn Reson Imaging 2023; 57:97-110. [PMID: 35633290 PMCID: PMC10083908 DOI: 10.1002/jmri.28273] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Breast cancer response to neoadjuvant chemotherapy (NAC) is typically evaluated through the assessment of tumor size reduction after a few cycles of NAC. In case of treatment ineffectiveness, this results in the patient suffering potentially severe secondary effects without achieving any actual benefit. PURPOSE To identify patients achieving pathologic complete response (pCR) after NAC by spatio-temporal radiomic analysis of dynamic contrast-enhanced (DCE) MRI images acquired before treatment. STUDY TYPE Single-center, retrospective. POPULATION A total of 251 DCE-MRI pretreatment images of breast cancer patients. FIELD STRENGTH/SEQUENCE 1.5 T/3 T, T1-weighted DCE-MRI. ASSESSMENT Tumor and peritumoral regions were segmented, and 348 radiomic features that quantify texture temporal variation, enhancement kinetics heterogeneity, and morphology were extracted. Based on subsets of features identified through forward selection, machine learning (ML) logistic regression models were trained separately with all images and stratifying on cancer molecular subtype and validated with leave-one-out cross-validation. STATISTICAL TESTS Feature significance was assessed using the Mann-Whitney U-test. Significance of the area under the receiver operating characteristics (ROC) curve (AUC) of the ML models was assessed using the associated 95% confidence interval (CI). Significance threshold was set to 0.05, adjusted with Bonferroni correction. RESULTS Nine features related to texture temporal variation and enhancement kinetics heterogeneity were significant in the discrimination of cases achieving pCR vs. non-pCR. The ML models achieved significant AUC of 0.707 (all cancers, n = 251, 59 pCR), 0.824 (luminal A, n = 107, 14 pCR), 0.823 (luminal B, n = 47, 15 pCR), 0.844 (HER2 enriched, n = 25, 11 pCR), 0.803 (triple negative, n = 72, 19 pCR). DATA CONCLUSIONS Differences in imaging phenotypes were found between complete and noncomplete responders. Furthermore, ML models trained per cancer subtype achieved high performance in classifying pCR vs. non-pCR cases. They may, therefore, have potential to help stratify patients according to the level of response predicted before treatment, pending further validation with larger prospective cohorts. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Luyi Han
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Yuan Gao
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | | | - Ritse M Mann
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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O'Donnell J, Gasior S, Davey M, O'Malley E, Lowery A, McGarry J, O'Connell A, Kerin M, McCarthy P. The accuracy of breast MRI radiomic methodologies in predicting pathological complete response to neoadjuvant chemotherapy: A systematic review and network meta-analysis. Eur J Radiol 2022; 157:110561. [DOI: 10.1016/j.ejrad.2022.110561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/13/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2022]
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22
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Braman N, Prasanna P, Bera K, Alilou M, Khorrami M, Leo P, Etesami M, Vulchi M, Turk P, Gupta A, Jain P, Fu P, Pennell N, Velcheti V, Abraham J, Plecha D, Madabhushi A. Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers. Clin Cancer Res 2022; 28:4410-4424. [PMID: 35727603 PMCID: PMC9588630 DOI: 10.1158/1078-0432.ccr-21-4148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/14/2022] [Accepted: 06/17/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy. EXPERIMENTAL DESIGN We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187). RESULTS Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response. CONCLUSIONS Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.
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Affiliation(s)
- Nathaniel Braman
- Case Western Reserve University, Cleveland, OH
- Picture Health, Cleveland, OH
| | - Prateek Prasanna
- Case Western Reserve University, Cleveland, OH
- Stony Brook University, New York, NY
| | - Kaustav Bera
- Case Western Reserve University, Cleveland, OH
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | | | | | - Patrick Leo
- Case Western Reserve University, Cleveland, OH
| | - Maryam Etesami
- Yale School of Medicine, Department of Radiology & Biomedical Imaging, New Haven, CT
| | - Manasa Vulchi
- The Cleveland Clinic Foundation (CCF), Cleveland, OH
| | - Paulette Turk
- The Cleveland Clinic Foundation (CCF), Cleveland, OH
| | - Amit Gupta
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Prantesh Jain
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Pingfu Fu
- Case Western Reserve University, Cleveland, OH
| | | | | | - Jame Abraham
- The Cleveland Clinic Foundation (CCF), Cleveland, OH
| | - Donna Plecha
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Case Western Reserve University, Cleveland, OH
- Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH
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23
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Howard FM, He G, Peterson JR, Pfeiffer JR, Earnest T, Pearson AT, Abe H, Cole JA, Nanda R. Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform. Breast Cancer Res Treat 2022; 196:57-66. [PMID: 36063220 PMCID: PMC9550684 DOI: 10.1007/s10549-022-06722-0] [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: 05/12/2022] [Accepted: 08/19/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of response to NAC has the potential to address this unmet need. METHODS We conducted a retrospective evaluation of a biophysical simulation model as a predictor of pCR. Patients who received standard NAC at the University of Chicago for EBC between January 1st, 2010 and March 31st, 2020 were included. Response was predicted using baseline breast MRI, clinicopathologic features, and treatment regimen by investigators who were blinded to patient outcomes. RESULTS A total of 144 tumors from 141 patients were included; 59 were triple-negative, 49 HER2-positive, and 36 hormone-receptor positive/HER2 negative. Lymph node disease was present in half of patients, and most were treated with an anthracycline-based regimen (58.3%). Sensitivity and specificity of the biophysical simulation for pCR were 88.0% (95% confidence interval [CI] 75.7 - 95.5) and 89.4% (95% CI 81.3 - 94.8), respectively, with robust results regardless of subtype. In patients with predicted pCR, 5-year event-free survival was 98%, versus 79% with predicted residual disease (log-rank p = 0.01, HR 4.57, 95% CI 1.36 - 15.34). At a median follow-up of 5.4 years, no patients with predicted pCR experienced disease recurrence. CONCLUSION A biophysical simulation model accurately predicts pCR and long-term outcomes from baseline MRI and clinical data, and is a promising tool to guide escalation/de-escalation of NAC.
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Affiliation(s)
- Frederick M Howard
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue
- MC 2115, Chicago, IL, 60637, USA.
| | - Gong He
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue
- MC 2115, Chicago, IL, 60637, USA
| | | | | | | | - Alexander T Pearson
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue
- MC 2115, Chicago, IL, 60637, USA
| | - Hiroyuki Abe
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | | | - Rita Nanda
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue
- MC 2115, Chicago, IL, 60637, USA
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Pesapane F, Agazzi GM, Rotili A, Ferrari F, Cardillo A, Penco S, Dominelli V, D'Ecclesiis O, Vignati S, Raimondi S, Bozzini A, Pizzamiglio M, Petralia G, Nicosia L, Cassano E. Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients With MRI-Radiomics: A Systematic Review and Meta-analysis. Curr Probl Cancer 2022; 46:100883. [PMID: 35914383 DOI: 10.1016/j.currproblcancer.2022.100883] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 12/30/2022]
Abstract
We performed a systematic review and a meta-analysis of studies using MRI-radiomics for predicting the pathological complete response in breast cancer patients undergoing neoadjuvant therapy , and we evaluated their methodological quality using the radiomics-quality-score (RQS). Random effects meta-analysis was performed pooling area under the receiver operating characteristics curves. Publication-bias was assessed using the Egger's test and visually inspecting the funnel plot. Forty-three studies were included in the qualitative review and 34 in the meta-analysis. Summary area under the receiver operating characteristics curve was 0,78 (95%CI:0,74-0,81). Heterogeneity according to the I2 statistic was substantial (71%) and there was no evidence of publication bias (P-value = 0,2). The average RQS was 12,7 (range:-1-26), with an intra-class correlation coefficient of 0.93 (95%CI:0.61-0.97). Year of publication, field intensity and synthetic RQS score do not appear to be moderators of the effect (P-value = 0.36, P-value = 0.28 and P-value = 0.92, respectively). MRI-radiomics may predict response to neoadjuvant therapy in breast cancer patients but the heterogeneity of the current studies is still substantial.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | | | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Andrea Cardillo
- Radiology Department, Università degli studi di Torino, Turin, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Oriana D'Ecclesiis
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Silvano Vignati
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Anna Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Pizzamiglio
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy; Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
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Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy. Cancers (Basel) 2022; 14:cancers14143515. [PMID: 35884576 PMCID: PMC9316501 DOI: 10.3390/cancers14143515] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/06/2022] [Accepted: 07/16/2022] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Neoadjuvant chemotherapy (NAC) followed with surgery is the standard strategy in the treatment of locally advanced breast cancer, but the individual efficacy varies. Early and accurate prediction of complete responders determines the NAC regimens and prognosis. Breast MRI has been recommended to monitor NAC response before, during, and after treatment. Radiomics has been heralded as a breakthrough in medicine and regarded to have changed the landscape of biomedical research in oncology. Delta-radiomics characterizing the change in feature values by applying radiomics to multiple time points, is a promising strategy for predicting response after NAC. In our study, the delta-radiomics model built with the change of radiomic features before and after one cycle NAC could effectively predict pathological complete response (pCR) in breast cancer. The model provides strong support for clinical decision-making at the earliest stage and helps patients benefit the most from NAC. Abstract Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.
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Herrero Vicent C, Tudela X, Moreno Ruiz P, Pedralva V, Jiménez Pastor A, Ahicart D, Rubio Novella S, Meneu I, Montes Albuixech Á, Santamaria MÁ, Fonfria M, Fuster-Matanzo A, Olmos Antón S, Martínez de Dueñas E. Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2022; 14:cancers14143508. [PMID: 35884572 PMCID: PMC9317428 DOI: 10.3390/cancers14143508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/07/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial, as pCR is a surrogate marker for survival. However, only 10–30% of patients achieve it. It is therefore essential to develop tools that enable to accurately predict response. Recently, different studies have demonstrated the feasibility of applying machine learning approaches to non-invasively predict pCR before NAC administration from magnetic resonance imaging (MRI) data. Some of those models are based on radiomics, an emerging field that allows the automated extraction of clinically relevant information from radiologic images. However, the research is still at an early stage and further data are needed. Here, we determine whether the combination of imaging data (radiomic features and perfusion/diffusion imaging biomarkers) extracted from multiparametric MRIs and clinical variables can improve pCR prediction to NAC compared to models only including imaging or clinical data, potentially avoiding unnecessary treatment and delays to surgery. Abstract Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR.
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Affiliation(s)
- Carmen Herrero Vicent
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
- Correspondence:
| | - Xavier Tudela
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Paula Moreno Ruiz
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Víctor Pedralva
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ana Jiménez Pastor
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Daniel Ahicart
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Silvia Rubio Novella
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Isabel Meneu
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ángela Montes Albuixech
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Miguel Ángel Santamaria
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - María Fonfria
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Almudena Fuster-Matanzo
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Santiago Olmos Antón
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Eduardo Martínez de Dueñas
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
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Reis J, Boavida J, Tran HT, Lyngra M, Reitsma LC, Schandiz H, Melles WA, Gjesdal KI, Geisler J, Geitung JT. Assessment of preoperative axillary nodal disease burden: breast MRI in locally advanced breast cancer before, during and after neoadjuvant endocrine therapy. BMC Cancer 2022; 22:702. [PMID: 35752785 PMCID: PMC9233812 DOI: 10.1186/s12885-022-09813-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/21/2022] [Indexed: 11/25/2022] Open
Abstract
Background Axillary lymph node (LN) metastasis is one of the most important predictors of recurrence and survival in breast cancer, and accurate assessment of LN involvement is crucial. Determining extent of residual disease is key for surgical planning after neoadjuvant therapy. The aim of the study was to evaluate the diagnostic reliability of MRI for nodal disease in locally advanced breast cancer patients treated with neoadjuvant endocrine therapy (NET). Methods Thirty-three clinically node-positive locally advanced breast cancer patients who underwent NET and surgery were prospectively enrolled. Two radiologists reviewed the axillary nodes at 3 separate time points MRI examinations at baseline (before the first treatment regimen), interim (following at least 2 months after the first cycle and prior to crossing-over), and preoperative (after the final administration of therapy and immediately before surgery). According to LN status after surgery, imaging features and diagnostic performance were analyzed. Results All 33 patients had a target LN reduction, the greatest treatment benefit from week 8 to week 16. There was a positive correlation between the maximal diameter of the most suspicious LN measured by MRI and pathology during and after NET, being highest at therapy completion (r = 0.6, P ≤ .001). Mean and median differences of maximal diameter of the most suspicious LN were higher with MRI than with pathology. Seven of 33 patients demonstrated normal posttreatment MRI nodal status (yrN0). Of these 7 yrN0, 3 exhibited no metastasis on final pathology (ypN0), 2 ypN1 and 2 ypN2. Reciprocally, MRI diagnosed 3 cases of ypN0 as yrN + . Diffusion -weighted imaging (DWI) was the only axillary node characteristic significant when associated with pathological node status (χ2(4) = 8.118, P = .072). Conclusion Performance characteristics of MRI were not completely sufficient to preclude surgical axillary staging. To our knowledge, this is the first study on MRI LN assessment following NET in locally advanced breast cancer, and further studies with larger sample sizes are required to consolidate the results of this preliminary study. Trial Registration Institutional Review Board approval was obtained (this current manuscript is from a prospective, open-label, randomized single-center cohort substudy of the NEOLETEXE trial). NEOLETEXE, a phase 2 clinical trial, was registered on March 23rd, 2015 in the National trial database of Norway and approved by the Regional Ethical Committee of the South-Eastern Health Region in Norway; registration number: REK-SØ-84–2015. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09813-9.
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Affiliation(s)
- Joana Reis
- Department of Diagnostic Imaging and Intervention, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway. .,Institute of Clinical Medicine, Campus AHUS, University of Oslo, Postboks 1000, 1478, Lørenskog, Norway. .,Translational Cancer Research Group, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway.
| | - Joao Boavida
- Department of Diagnostic Imaging and Intervention, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
| | - Hang T Tran
- Department of Diagnostic Imaging and Intervention, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
| | - Marianne Lyngra
- Department of Pathology, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
| | - Laurens Cornelus Reitsma
- Department of Breast and Endocrine Surgery, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
| | - Hossein Schandiz
- Department of Pathology, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
| | - Woldegabriel A Melles
- Department of Diagnostic Imaging and Intervention, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
| | - Kjell-Inge Gjesdal
- Department of Diagnostic Imaging and Intervention, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway.,Sunnmøre MR-Clinic, Agrinorbygget, Langelansveg 15, 6010, Ålesund, Norway
| | - Jürgen Geisler
- Institute of Clinical Medicine, Campus AHUS, University of Oslo, Postboks 1000, 1478, Lørenskog, Norway.,Translational Cancer Research Group, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway.,Department of Oncology, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
| | - Jonn Terje Geitung
- Department of Diagnostic Imaging and Intervention, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway.,Institute of Clinical Medicine, Campus AHUS, University of Oslo, Postboks 1000, 1478, Lørenskog, Norway.,Translational Cancer Research Group, Akershus University Hospital (AHUS), Postboks 1000, 1478, Lørenskog, Norway
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28
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Massafra R, Comes MC, Bove S, Didonna V, Gatta G, Giotta F, Fanizzi A, La Forgia D, Latorre A, Pastena MI, Pomarico D, Rinaldi L, Tamborra P, Zito A, Lorusso V, Paradiso AV. Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy. J Pers Med 2022; 12:jpm12060953. [PMID: 35743737 PMCID: PMC9225219 DOI: 10.3390/jpm12060953] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/24/2022] [Accepted: 06/07/2022] [Indexed: 02/04/2023] Open
Abstract
To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.
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Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
- Correspondence: (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Correspondence: (A.F.); (D.L.F.)
| | - Agnese Latorre
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Angelo Virgilio Paradiso
- Oncologia Sperimentale e Biobanca, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
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29
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Zhong X, Long H, Su L, Zheng R, Wang W, Duan Y, Hu H, Lin M, Xie X. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 2022; 47:2071-2088. [PMID: 35364684 DOI: 10.1007/s00261-022-03496-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models. RESULTS Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82-0.89), 0.87 (95% CI 0.83-0.92), and 0.74 (95% CI 0.67-0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region. CONCLUSION Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.
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Affiliation(s)
- Xian Zhong
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Haiyi Long
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Liya Su
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Ruiying Zheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Yu Duan
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Hangtong Hu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Manxia Lin
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
| | - Xiaoyan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
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30
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A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis. Cancers (Basel) 2022; 14:cancers14112731. [PMID: 35681711 PMCID: PMC9179305 DOI: 10.3390/cancers14112731] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx. Abstract Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.
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31
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Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2022; 49:2820-2835. [PMID: 34455593 PMCID: PMC8882689 DOI: 10.1002/mp.15195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/31/2023] Open
Abstract
Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.
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Affiliation(s)
- Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Jana G. Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, 10993 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Kalina V. Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Megan E. Poorman
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Prathyush Chirra
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
| | - Bettina Baessler
- University Hospital of Zurich and University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Jessica Winfield
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
| | - Satish E. Viswanath
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Nandita M. deSouza
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
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32
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Qi TH, Hian OH, Kumaran AM, Tan TJ, Cong TRY, Su-Xin GL, Lim EH, Ng R, Yeo MCR, Tching FLLW, Zewen Z, Hui CYS, Xin WR, Ooi SKG, Leong LCH, Tan SM, Preetha M, Sim Y, Tan VKM, Yeong J, Yong WF, Cai Y, Nei WL. Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer. Breast Cancer Res Treat 2022; 193:121-138. [PMID: 35262831 DOI: 10.1007/s10549-022-06521-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/31/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors' response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. METHODS The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. RESULTS The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). CONCLUSIONS Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.
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Affiliation(s)
- Tan Hong Qi
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Ong Hiok Hian
- School of Computer Science and Engineering, Nanyang Technological University Singapore, Singapore, Singapore
| | - Arjunan Muthu Kumaran
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Tira J Tan
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Tan Ryan Ying Cong
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Ghislaine Lee Su-Xin
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore
| | - Raymond Ng
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Ming Chert Richard Yeo
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Faye Lynette Lim Wei Tching
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Zhang Zewen
- Division of Medical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Christina Yang Shi Hui
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Wong Ru Xin
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Su Kai Gideon Ooi
- Division of Oncologic Imaging, National Cancer Center Singapore, Singapore, Singapore.,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Lester Chee Hao Leong
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Su Ming Tan
- Division of Breast Surgery, Changi General Hospital, Singapore, Singapore
| | - Madhukumar Preetha
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Yirong Sim
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Veronique Kiak Mien Tan
- Division of Surgery and Surgical Oncology, National Cancer Center Singapore, Singapore, Singapore.,Department of Breast Surgery, Singapore General Hospital, Singapore, Singapore
| | - Joe Yeong
- Division of Pathology, Singapore General Hospital, Singapore, Singapore.,Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore, Singapore
| | - Wong Fuh Yong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore. .,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore.
| | - Yiyu Cai
- School of Mechanical & Aerospace Engineering, Nanyang Technological University Singapore, Singapore, Singapore.
| | - Wen Long Nei
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore. .,Oncology Academic Programme, Duke-NUS Medical School, Singapore, Singapore.
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Janssen BV, Verhoef S, Wesdorp NJ, Huiskens J, de Boer OJ, Marquering H, Stoker J, Kazemier G, Besselink MG. Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review. Ann Surg 2022; 275:560-567. [PMID: 34954758 DOI: 10.1097/sla.0000000000005349] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC. SUMMARY OF BACKGROUND DATA Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection. METHODS A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score. RESULTS After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36. CONCLUSIONS The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved.
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Affiliation(s)
- Boris V Janssen
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Severano Verhoef
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Nina J Wesdorp
- Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | | | - Onno J de Boer
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Henk Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jaap Stoker
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Marc G Besselink
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
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Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review. Cancers (Basel) 2022; 14:cancers14030665. [PMID: 35158932 PMCID: PMC8833587 DOI: 10.3390/cancers14030665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Ultrasound (US) is a non-invasive imaging method that is routinely utilized in head and neck cancer patients to assess the anatomic extent of tumors, nodal and non-nodal neck masses and for imaging the salivary glands. In this review, we summarize the present evidence on whether the application of machine learning (ML) methods can potentially improve the performance of US in head and neck cancer patients. We found that published clinical literature on ML methods applied to US datasets was limited but showed evidence of improved diagnostic and prognostic performance. However, a majority of these studies were based on retrospective evaluation and conducted at a single center with a limited number of datasets. The conduct of multi-center studies could help better validate the performance of ML-based US radiomics and facilitate the integration of these approaches into routine clinical practice. Abstract Radiomics is a rapidly growing area of research within radiology that involves the extraction and modeling of high-dimensional quantitative imaging features using machine learning/artificial intelligence (ML/AI) methods. In this review, we describe the published clinical evidence on the application of ML methods to improve the performance of ultrasound (US) in head and neck oncology. A systematic search of electronic databases (MEDLINE, PubMed, clinicaltrials.gov) was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Of 15,080 initial articles identified, 34 studies were selected for in-depth analysis. Twenty-five out of 34 studies (74%) focused on the diagnostic application of US radiomics while 6 (18%) studies focused on response assessment and 3 (8%) studies utilized US radiomics for modeling normal tissue toxicity. Support vector machine (SVM) was the most commonly employed ML method (47%) followed by multivariate logistic regression (24%) and k-nearest neighbor analysis (21%). Only 11/34 (~32%) of the studies included an independent validation set. A majority of studies were retrospective in nature (76%) and based on single-center evaluation (85%) with variable numbers of patients (12–1609) and imaging datasets (32–1624). Despite these limitations, the application of ML methods resulted in improved diagnostic and prognostic performance of US highlighting the potential clinical utility of this approach.
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Granzier RWY, Ibrahim A, Primakov S, Keek SA, Halilaj I, Zwanenburg A, Engelen SME, Lobbes MBI, Lambin P, Woodruff HC, Smidt ML. Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability. J Magn Reson Imaging 2021; 56:592-604. [PMID: 34936160 PMCID: PMC9544420 DOI: 10.1002/jmri.28027] [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: 08/27/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 12/14/2022] Open
Abstract
Background Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. Objective Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test–retest measurements. Study Type Prospective. Population 11 healthy female volunteers. Field Strength/Sequence 1.5 T; MRI exams, comprising T2‐weighted turbo spin‐echo (T2W) sequence, native T1‐weighted turbo gradient‐echo (T1W) sequence, diffusion‐weighted imaging (DWI) sequence using b‐values 0/150/800, and corresponding derived ADC maps. Assessment 18 MRI exams (three test–retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z‐score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z‐score normalization + grayscale discretization using 32 and 64 bins with and without BFC. Statistical Tests Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut‐off value of CCC > 0.90. Results Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z‐score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. Data Conclusion Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. Level of Evidence 2 Technical Efficacy Stage 1
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Affiliation(s)
- R W Y Granzier
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - A Ibrahim
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium.,Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - S Primakov
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - S A Keek
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - I Halilaj
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Health Innovation Ventures, Maastricht, The Netherlands
| | - A Zwanenburg
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden, Rossendorf, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - S M E Engelen
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - M B I Lobbes
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands
| | - P Lambin
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - H C Woodruff
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - M L Smidt
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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Hadar T, Koretz M, Nawass M, Allweis TM. Innovative Standards in Surgery of the Breast after Neoadjuvant Systemic Therapy. Breast Care (Basel) 2021; 16:590-597. [PMID: 35087362 PMCID: PMC8739938 DOI: 10.1159/000520051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/29/2021] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND The goal of neoadjuvant systemic therapy (NST) in breast cancer is to downstage tumors and downgrade treatment. Indications are constantly evolving. These changes raise practical questions for planning of surgery after NST. SUMMARY In this review we discuss current evolving aspects of surgery of the breast after NST. Breast-conserving surgery (BCS) eligibility increases after NST - both neoadjuvant chemotherapy (NAC) and neoadjuvant endocrine therapy. Adequate margin width in NST and upfront surgery are similar - "no tumor on ink" for invasive cancer. Oncoplastic breast surgery after NST is feasible - both for BCS and mastectomy with reconstruction. There is increasing interest in the possibility of omitting surgery in patients with a complete response to NAC. Several trials are being conducted in aim of achieving acceptable prediction of pathological complete response, by combination of imaging and percutaneous biopsy of the tumor bed, as well as assessing the safety of such an approach. KEY MESSAGES Surgery of the breast after NST should be determined not only according to biologic and anatomic parameters at diagnosis, but is dynamic, and must be tailored according to the response to therapy. The omission of surgery in exceptional responders after NAC is being explored.
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Affiliation(s)
- Tal Hadar
- Department of Breast Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Michael Koretz
- Department of Breast Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Mahmood Nawass
- Department of Breast Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Tanir M. Allweis
- Department of Breast Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University, Jerusalem, Israel
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Davey MS, Davey MG, Ryan ÉJ, Hogan AM, Kerin MJ, Joyce M. The use of radiomic analysis of magnetic resonance imaging in predicting distant metastases of rectal carcinoma following surgical resection: A systematic review and meta-analysis. Colorectal Dis 2021; 23:3065-3072. [PMID: 34536962 DOI: 10.1111/codi.15919] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/03/2021] [Accepted: 09/12/2021] [Indexed: 12/24/2022]
Abstract
AIM Estimating prognosis in rectal carcinoma (RC) is challenging, with distant recurrence (DR) occurring in up to 30% of cases. Radiomics is a novel field using diagnostic imaging to investigate the tumour heterogeneity of cancers and may have the potential to predict DR. The aim of the study was to perform a systematic review of the current literature evaluating the use of radiomics in predicting DR in patients with resected RC. METHODS A systematic review was performed as per PRISMA guidelines to identify studies reporting radiomic analysis of magnetic resonance imaging (MRI) to predict DR in patients diagnosed with RC. Sensitivity and specificity of radiomic analyses were included for meta-analysis. RESULTS A total of seven studies including 1497 patients (998 males) were included, seven, five and one of whom reported radiomics, respectively. The overall pooled rate of DR from all included studies was 17.1% (256/1497), with 15.6% (236/1497), 1.3% (19/1497) and 0.2% (3/1497) of patients having hepatic, pulmonary and peritoneal metastases. Meta-analysis demonstrated that radiomics correctly predicted DR with pooled sensitivities and specificities of MRI 0.76 (95% CI: 0.73, 0.78) and 0.85 (95% CI: 0.83, 0.88), respectively. CONCLUSION This systematic review suggests the benefit of radiomic analysis of preoperative MRI in identifying patients with resected RC at an increased risk of DR. Our findings warrant validation in larger prospective studies as modalities to predict DR is a significant unmet need in RC. Radiomics may allow for tailored therapeutic strategies for high-risk groups.
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Affiliation(s)
- Martin S Davey
- Discipline of Surgery, Galway University Hospitals, Galway, Ireland
| | - Matthew G Davey
- Discipline of Surgery, Galway University Hospitals, Galway, Ireland
| | - Éanna J Ryan
- Discipline of Surgery, Galway University Hospitals, Galway, Ireland
| | - Aisling M Hogan
- Discipline of Surgery, Galway University Hospitals, Galway, Ireland
| | - Michael J Kerin
- Discipline of Surgery, Galway University Hospitals, Galway, Ireland
| | - Myles Joyce
- Discipline of Surgery, Galway University Hospitals, Galway, Ireland
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Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021; 127:39-56. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.
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Affiliation(s)
- Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2021; 19:132-146. [PMID: 34663898 DOI: 10.1038/s41571-021-00560-7] [Citation(s) in RCA: 237] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2021] [Indexed: 12/14/2022]
Abstract
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.
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Affiliation(s)
- Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Tempus Labs, Chicago, IL, USA
| | - Amit Gupta
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. .,Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH, USA.
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Si L, Zhong J, Huo J, Xuan K, Zhuang Z, Hu Y, Wang Q, Zhang H, Yao W. Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Eur Radiol 2021; 32:1353-1361. [PMID: 34347157 DOI: 10.1007/s00330-021-08190-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/15/2021] [Accepted: 05/07/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint. MATERIALS AND METHODS A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics. RESULTS A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660-0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set. CONCLUSIONS The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future. KEY POINTS • Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies.
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Affiliation(s)
- Liping Si
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China
| | - Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China
| | - Jiayu Huo
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Huashan Road #1954, Shanghai, 200030, China
| | - Kai Xuan
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Huashan Road #1954, Shanghai, 200030, China
| | - Zixu Zhuang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Huashan Road #1954, Shanghai, 200030, China
| | - Yangfan Hu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Qian Wang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Huashan Road #1954, Shanghai, 200030, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China.
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Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
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Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future. ACTA ACUST UNITED AC 2021; 28:2351-2372. [PMID: 34202321 PMCID: PMC8293249 DOI: 10.3390/curroncol28040217] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/14/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022]
Abstract
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.
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Abdurixiti M, Nijiati M, Shen R, Ya Q, Abuduxiku N, Nijiati M. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol 2021; 94:20201272. [PMID: 33882244 DOI: 10.1259/bjr.20201272] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES To assess the methodological quality of radiomic studies based on positron emission tomography/computed tomography (PET/CT) images predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). METHODS We systematically searched for eligible studies in the PubMed and Web of Science datasets using the terms "radiomics", "PET/CT", "NSCLC", and "EGFR". The included studies were screened by two reviewers independently. The quality of the radiomic workflow of studies was assessed using the Radiomics Quality Score (RQS). Interclass correlation coefficient (ICC) was used to determine inter rater agreement for the RQS. An overview of the methodologies used in steps of the radiomics workflow and current results are presented. RESULTS Six studies were included with sample sizes of 973 ranging from 115 to 248 patients. Methodologies in the radiomic workflow varied greatly. The first-order statistics were the most reproducible features. The RQS scores varied from 13.9 to 47.2%. All studies were scored below 50% due to defects on multiple segmentations, phantom study on all scanners, imaging at multiple time points, cut-off analyses, calibration statistics, prospective study, potential clinical utility, and cost-effectiveness analysis. The ICC results for majority of RQS items were excellent. The ICC for summed RQS was 0.986 [95% confidence interval (CI): 0.898-0.998]. CONCLUSIONS The PET/CT-based radiomics signature could serve as a diagnostic indicator of EGFR mutation status in NSCLC patients. However, the current conclusions should be interpreted with care due to the suboptimal quality of the studies. Consensus for standardization of PET/CT-based radiomic workflow for EGFR mutation status in NSCLC patients is warranted to further improve research. ADVANCES IN KNOWLEDGE Radiomics can offer clinicians better insight into the prediction of EGFR mutation status in NSCLC patients, whereas the quality of relative studies should be improved before application to the clinical setting.
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Affiliation(s)
- Meilinuer Abdurixiti
- Department of Nuclear Medicine, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Mayila Nijiati
- Department of Otolaryngology, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Rongfang Shen
- Department of Nuclear Medicine, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Qiu Ya
- Department of Radiology, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Naibijiang Abuduxiku
- Department of Nuclear Medicine, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
| | - Mayidili Nijiati
- Department of Radiology, The First People's Hospital of Kashi Area, Kashi, Xinjiang, China
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Fan M, Chen H, You C, Liu L, Gu Y, Peng W, Gao X, Li L. Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer. Front Mol Biosci 2021; 8:622219. [PMID: 33869279 PMCID: PMC8044916 DOI: 10.3389/fmolb.2021.622219] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 02/15/2021] [Indexed: 01/23/2023] Open
Abstract
Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development (n = 61) dataset and a testing dataset (n = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Hang Chen
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Li Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis. Neuroradiology 2021; 63:1293-1304. [PMID: 33649882 PMCID: PMC8295153 DOI: 10.1007/s00234-021-02668-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/03/2021] [Indexed: 02/07/2023]
Abstract
Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-021-02668-0.
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Liefaard MC, Lips EH, Wesseling J, Hylton NM, Lou B, Mansi T, Pusztai L. The Way of the Future: Personalizing Treatment Plans Through Technology. Am Soc Clin Oncol Educ Book 2021; 41:1-12. [PMID: 33793316 DOI: 10.1200/edbk_320593] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.
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Affiliation(s)
- Marte C Liefaard
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
| | - Bin Lou
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ
| | - Tommaso Mansi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ
| | - Lajos Pusztai
- Yale Cancer Center, Yale School of Medicine, New Haven, CT
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Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer. Eur Radiol 2021; 31:7031-7038. [PMID: 33569624 DOI: 10.1007/s00330-021-07724-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/24/2020] [Accepted: 01/27/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data. METHODS Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2Wvolume/T2Wentropy/ADCmean/SUVmean/TLG/CTmean-HU) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset. RESULTS When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture). CONCLUSION In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research. KEY POINTS • Quantification of local tumour texture on pre-therapy FDG-PET/CT and MRI has potential added value compared to global tumour features to predict response to chemoradiotherapy in rectal cancer. • However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture over global tumour features is limited. • Predictive performance of our optimal model-combining clinical baseline variables with global quantitative tumour features-was encouraging (AUC 0.83), warranting further research in this direction on a larger scale.
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Dietzel M, Clauser P, Kapetas P, Schulz-Wendtland R, Baltzer PAT. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. ROFO-FORTSCHR RONTG 2021; 193:898-908. [PMID: 33535260 DOI: 10.1055/a-1346-0095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. METHODS AND RESULTS This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. CONCLUSION Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. KEY POINTS · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review.. CITATION FORMAT · Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.
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Affiliation(s)
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | | | - Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2020; 48:1785-1794. [PMID: 33326049 PMCID: PMC8113210 DOI: 10.1007/s00259-020-05142-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/29/2020] [Indexed: 02/08/2023]
Abstract
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05142-w.
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Saint Martin MJ, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 34:355-366. [PMID: 33180226 DOI: 10.1007/s10334-020-00892-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/27/2020] [Accepted: 10/23/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. MATERIALS AND METHODS T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions. RESULTS Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms. DISCUSSION A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
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Affiliation(s)
- Marie-Judith Saint Martin
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France.
| | - Fanny Orlhac
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Pia Akl
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
- HCL, Radiologie du Groupement Hospitalier Est, Hôpital Femme Mère Enfant, Unité Fonctionnelle: Imagerie de la Femme, 3 Quai des Célestins, 69002, Lyon, France
- Institut Curie, Service de Radiodiagnostic, 26 rue d'Ulm, 75005, Paris, France
| | - Fahad Khalid
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Christophe Nioche
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Irène Buvat
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Caroline Malhaire
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
- Institut Curie, Service de Radiodiagnostic, 26 rue d'Ulm, 75005, Paris, France
| | - Frédérique Frouin
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
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