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Moslemi A, Osapoetra LO, Dasgupta A, Alberico D, Trudeau M, Gandhi S, Eisen A, Wright F, Look-Hong N, Curpen B, Kolios MC, Czarnota GJ. Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning. Front Oncol 2024; 14:1359148. [PMID: 38756659 PMCID: PMC11096486 DOI: 10.3389/fonc.2024.1359148] [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: 12/20/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Objective Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT). Materials and methods Several deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes. Results Amongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network. Conclusion Deep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.
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Affiliation(s)
- Amir Moslemi
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Archya Dasgupta
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - David Alberico
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Maureen Trudeau
- Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Sonal Gandhi
- Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Andrea Eisen
- Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Frances Wright
- Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Nicole Look-Hong
- Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Michael C. Kolios
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
| | - Gregory J. Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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Saednia K, Tran WT, Sadeghi-Naini A. A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies. Med Phys 2023; 50:7852-7864. [PMID: 37403567 DOI: 10.1002/mp.16574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 06/06/2023] [Accepted: 06/10/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival. PURPOSE This study, for the first time, proposes a hierarchical self-attention-guided deep learning framework to predict NAC response in breast cancer patients using digital histopathological images of pre-treatment biopsy specimens. METHODS Digitized hematoxylin and eosin-stained slides of BC core needle biopsies were obtained from 207 patients treated with NAC, followed by surgery. The response to NAC for each patient was determined using the standard clinical and pathological criteria after surgery. The digital pathology images were processed through the proposed hierarchical framework consisting of patch-level and tumor-level processing modules followed by a patient-level response prediction component. A combination of convolutional layers and transformer self-attention blocks were utilized in the patch-level processing architecture to generate optimized feature maps. The feature maps were analyzed through two vision transformer architectures adapted for the tumor-level processing and the patient-level response prediction components. The feature map sequences for these transformer architectures were defined based on the patch positions within the tumor beds and the bed positions within the biopsy slide, respectively. A five-fold cross-validation at the patient level was applied on the training set (144 patients with 9430 annotated tumor beds and 1,559,784 patches) to train the models and optimize the hyperparameters. An unseen independent test set (63 patients with 3574 annotated tumor beds and 173,637 patches) was used to evaluate the framework. RESULTS The obtained results on the test set showed an AUC of 0.89 and an F1-score of 90% for predicting pCR to NAC a priori by the proposed hierarchical framework. Similar frameworks with the patch-level, patch-level + tumor-level, and patch-level + patient-level processing components resulted in AUCs of 0.79, 0.81, and 0.84 and F1-scores of 86%, 87%, and 89%, respectively. CONCLUSIONS The results demonstrate a high potential of the proposed hierarchical deep-learning methodology for analyzing digital pathology images of pre-treatment tumor biopsies to predict the pathological response of breast cancer to NAC.
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Affiliation(s)
- Khadijeh Saednia
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Ontario, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - William T Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Temerity Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ali Sadeghi-Naini
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Ontario, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Temerity Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
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Champendal M, Marmy L, Malamateniou C, Sá Dos Reis C. Artificial intelligence to support person-centred care in breast imaging - A scoping review. J Med Imaging Radiat Sci 2023; 54:511-544. [PMID: 37183076 DOI: 10.1016/j.jmir.2023.04.001] [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: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023]
Abstract
AIM To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies. METHODS The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage. RESULTS 3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used. CONCLUSIONS The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.
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Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Laurent Marmy
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Christina Malamateniou
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, UK.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
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Jha AK, Mithun S, Sherkhane UB, Dwivedi P, Puts S, Osong B, Traverso A, Purandare N, Wee L, Rangarajan V, Dekker A. Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:569-582. [PMID: 37720353 PMCID: PMC10501896 DOI: 10.37349/etat.2023.00153] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Umeshkumar B. Sherkhane
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
| | - Pooj Dwivedi
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
- Department of Nuclear Medicine, Advance Center for Treatment, Research, Education in Cancer, Kharghar, Navi-Mumbai 410210, Maharashtra, India
| | - Senders Puts
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Biche Osong
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Nilendu Purandare
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
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Liao J, Qin QH, Lv FY, Huang Z, Lian B, Wei CY, Mo QG, Tan QX. IKKα inhibition re-sensitizes acquired adriamycin-resistant triple negative breast cancer cells to chemotherapy-induced apoptosis. Sci Rep 2023; 13:6211. [PMID: 37069240 PMCID: PMC10110611 DOI: 10.1038/s41598-023-33358-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/12/2023] [Indexed: 04/19/2023] Open
Abstract
IKKα has been shown to be responsible of multiple pro-tumorigenic functions and therapy resistance independent of canonical NF-κB, but its role in acquired chemotherapy resistance in breast cancer remains unclarified. In this study, we obtained pre-treatment biopsy and post-treatment mastectomy specimens from a retrospective cohort of triple-negative breast cancer (TNBC) patients treated with neoadjuvant chemotherapy(NAC) (n = 43). Immunohistochemical methods were used to detect the expression of IKKα before and after NAC, and the relationship between IKKα and the pathologic response to NAC was examined. In addition, we developed a new ADR-resistant MDA-MB-231 cell line(MDA-MB-231/ADR) and analyzed these cells for changes in IKKα expression, the role and mechanisms of the increased IKKα in promoting drug resistance were determined in vitro and in vivo. We demonstrated that the expression of IKKα in residual TNBC tissues after chemotherapy was significantly higher than that before chemotherapy, and was positively correlated with lower pathological reaction. IKKα expression was significantly higher in ADR-resistant TNBC cells than in ADR-sensitive cells, IKKα knockdown results in apoptotic cell death of chemoresistant cells upon drug treatment. Moreover, IKKα knockdown promotes chemotherapeutic drug-induced tumor cell death in an transplanted tumor mouse model. Functionally, we demonstrated that IKKα knockdown significantly upregulated the expression of cleaved caspase 3 and Bax and inhibited the expression of Bcl-2 upon ADR treatment. Our findings highlighted that IKKα exerts an important and previously unknown role in promoting chemoresistance in TNBC, combining IKKα inhibition with chemotherapy may be an effective strategy to improve treatment outcome in chemoresistant TNBC patients.
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Affiliation(s)
- Jian Liao
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China
| | - Qing-Hong Qin
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China
- Key Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi, Department of Education, Nanning, 530021, Guangxi Province, People's Republic of China
| | - Fa-You Lv
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China
- Key Laboratory of Breast Cancer Diagnosis and Treatment Research of Guangxi, Department of Education, Nanning, 530021, Guangxi Province, People's Republic of China
| | - Zhen Huang
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China
| | - Bin Lian
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China
| | - Chang-Yuan Wei
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China
| | - Qin-Guo Mo
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China.
| | - Qi-Xing Tan
- Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, 530021, Guangxi Province, People's Republic of China.
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Pavlov MV, Bavrina AP, Plekhanov VI, Golubyatnikov GY, Orlova AG, Subochev PV, Davydova DA, Turchin IV, Maslennikova AV. Changes in the tumor oxygenation but not in the tumor volume and tumor vascularization reflect early response of breast cancer to neoadjuvant chemotherapy. Breast Cancer Res 2023; 25:12. [PMID: 36717842 PMCID: PMC9887770 DOI: 10.1186/s13058-023-01607-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/17/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Breast cancer neoadjuvant chemotherapy (NACT) allows for assessing tumor sensitivity to systemic treatment, planning adjuvant treatment and follow-up. However, a sufficiently large number of patients fail to achieve the desired level of pathological tumor response while optimal early response assessment methods have not been established now. In our study, we simultaneously assessed the early chemotherapy-induced changes in the tumor volume by ultrasound (US), the tumor oxygenation by diffuse optical spectroscopy imaging (DOSI), and the state of the tumor vascular bed by Doppler US to elaborate the predictive criteria of breast tumor response to treatment. METHODS A total of 133 patients with a confirmed diagnosis of invasive breast cancer stage II to III admitted to NACT following definitive breast surgery were enrolled, of those 103 were included in the final analysis. Tumor oxygenation by DOSI, tumor volume by US, and tumor vascularization by Doppler US were determined before the first and second cycle of NACT. After NACT completion, patients underwent surgery followed by pathological examination and assessment of the pathological tumor response. On the basis of these, data regression predictive models were created. RESULTS We observed changes in all three parameters 3 weeks after the start of the treatment. However, a high predictive potential for early assessment of tumor sensitivity to NACT demonstrated only the level of oxygenation, ΔStO2, (ρ = 0.802, p ≤ 0.01). The regression model predicts the tumor response with a high probability of a correct conclusion (89.3%). The "Tumor volume" model and the "Vascularization index" model did not accurately predict the absence of a pathological tumor response to treatment (60.9% and 58.7%, respectively), while predicting a positive response to treatment was relatively better (78.9% and 75.4%, respectively). CONCLUSIONS Diffuse optical spectroscopy imaging appeared to be a robust tool for early predicting breast cancer response to chemotherapy. It may help identify patients who need additional molecular genetic study of the tumor in order to find the source of resistance to treatment, as well as to correct the treatment regimen.
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Affiliation(s)
- Mikhail V. Pavlov
- Nizhny Novgorod Regional Clinical Oncology Dispensary, Delovaya St., 11/1, Nizhny Novgorod, Russia 603126
| | - Anna P. Bavrina
- grid.416347.30000 0004 0386 1631Privolzhsky Research Medical University, Minina Square, 10/1, Nizhny Novgorod, Russia 603950
| | - Vladimir I. Plekhanov
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - German Yu. Golubyatnikov
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Anna G. Orlova
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Pavel V. Subochev
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Diana A. Davydova
- Nizhny Novgorod Regional Clinical Oncology Dispensary, Delovaya St., 11/1, Nizhny Novgorod, Russia 603126
| | - Ilya V. Turchin
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Anna V. Maslennikova
- grid.416347.30000 0004 0386 1631Privolzhsky Research Medical University, Minina Square, 10/1, Nizhny Novgorod, Russia 603950 ,grid.28171.3d0000 0001 0344 908XNational Research Lobachevsky State University of Nizhny Novgorod, Gagarin Ave., 23, Nizhny Novgorod, Russia 603022
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Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des 2023; 101:175-194. [PMID: 36303299 DOI: 10.1111/cbdd.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
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Jha AK, Mithun S, Purandare NC, Kumar R, Rangarajan V, Wee L, Dekker A. Radiomics: a quantitative imaging biomarker in precision oncology. Nucl Med Commun 2022; 43:483-493. [PMID: 35131965 DOI: 10.1097/mnm.0000000000001543] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Nilendu C Purandare
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Science, New Delhi, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
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Jaberipour M, Soliman H, Sahgal A, Sadeghi-Naini A. A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning. Sci Rep 2021; 11:21620. [PMID: 34732781 PMCID: PMC8566533 DOI: 10.1038/s41598-021-01024-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 10/21/2021] [Indexed: 12/14/2022] Open
Abstract
This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict local failure in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The predictive models were developed using the data from 100 patients (141 lesions) and evaluated on an independent test set with data from 20 patients (30 lesions). Quantitative MRI radiomic features were derived from the treatment-planning contrast-enhanced T1w and T2-FLAIR images. A multi-phase feature reduction and selection procedure was applied to construct an optimal quantitative MRI biomarker for predicting therapy outcome. The performance of standard clinical features in therapy outcome prediction was evaluated using a similar procedure. Survival analyses were conducted to compare the long-term outcome of the two patient cohorts (local control/failure) identified based on prediction at pre-treatment, and standard clinical criteria at last patient follow-up after SRT. The developed quantitative MRI biomarker consists of four features with two features quantifying heterogeneity in the edema region, one feature characterizing intra-tumour heterogeneity, and one feature describing tumour morphology. The predictive models with the radiomic and clinical feature sets yielded an AUC of 0.87 and 0.62, respectively on the independent test set. Incorporating radiomic features into the clinical predictive model improved the AUC of the model by up to 16%, relatively. A statistically significant difference was observed in survival of the two patient cohorts identified at pre-treatment using the radiomics-based predictive model, and at post-treatment using the the RANO-BM criteria. Results of this study revealed a good potential for quantitative MRI radiomic features at pre-treatment in predicting local failure in relatively large brain metastases undergoing SRT, and is a step forward towards a precision oncology paradigm for brain metastasis.
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Affiliation(s)
- Majid Jaberipour
- grid.21100.320000 0004 1936 9430Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada
| | - Hany Soliman
- grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, ON Canada
| | - Arjun Sahgal
- grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, ON Canada
| | - Ali Sadeghi-Naini
- grid.21100.320000 0004 1936 9430Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
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10
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Li F, Yang Y, Wei Y, He P, Chen J, Zheng Z, Bu H. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl Med 2021; 19:348. [PMID: 34399795 PMCID: PMC8365907 DOI: 10.1186/s12967-021-03020-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/02/2021] [Indexed: 02/08/2023] Open
Abstract
Background Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance. Methods In total, 540 breast cancer patients receiving standard NAC were enrolled. Based on H&E-stained images, DL methods were employed to automatically identify tumor epithelium and predict pCR by scoring the identified tumor epithelium to produce a histopathological biomarker, the pCR-score. The predictive performance of the pCR-score was assessed and compared with that of conventional biomarkers including stromal tumor-infiltrating lymphocytes (sTILs) and subtype. Results The pCR-score derived from H&E staining achieved an area under the curve (AUC) of 0.847 in predicting pCR directly, and achieved accuracy, F1 score, and AUC of 0.853, 0.503, and 0.822 processed by the logistic regression method, respectively, higher than either sTILs or subtype; a prediction model of pCR constructed by integrating sTILs, subtype and pCR-score yielded a mean AUC of 0.890, outperforming the baseline sTIL-subtype model by 0.051 (0.839, P = 0.001). Conclusion The DL-based pCR-score from histological images is predictive of pCR better than sTILs and subtype, and holds the great potentials for a more accurate stratification of patients for NAC. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03020-z.
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Affiliation(s)
- Fengling Li
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.,Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yongquan Yang
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yani Wei
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.,Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ping He
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jie Chen
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhongxi Zheng
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Hong Bu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China. .,Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
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11
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Moghadas-Dastjerdi H, Rahman SETH, Sannachi L, Wright FC, Gandhi S, Trudeau ME, Sadeghi-Naini A, Czarnota GJ. Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning. Transl Oncol 2021; 14:101183. [PMID: 34293685 PMCID: PMC8319580 DOI: 10.1016/j.tranon.2021.101183] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 07/07/2021] [Accepted: 07/13/2021] [Indexed: 01/01/2023] Open
Abstract
Textural and second derivative textural features of CT images can be used in conjunction with machine learning models to predict breast cancer response to chemotherapy prior to the start of treatment. The proposed predictive model separates the patients at pre-treatment into two cohorts (responders/non-responders) with significantly different survival. The proposed methodology is a step forward towards the precision oncology paradigm for breast cancer patients.
Although neoadjuvant chemotherapy (NAC) is a crucial component of treatment for locally advanced breast cancer (LABC), only about 70% of patients respond to it. Effective adjustment of NAC for individual patients can significantly improve survival rates of those resistant to standard regimens. Thus, the early prediction of NAC outcome is of great importance in facilitating a personalized paradigm for breast cancer therapeutics. In this study, quantitative computed tomography (qCT) parametric imaging in conjunction with machine learning techniques were investigated to predict LABC tumor response to NAC. Textural and second derivative textural (SDT) features of CT images of 72 patients diagnosed with LABC were analysed before the initiation of NAC to quantify intra-tumor heterogeneity. These quantitative features were processed through a correlation-based feature reduction followed by a sequential feature selection with a bootstrap 0.632+ area under the receiver operating characteristic (ROC) curve (AUC0.632+) criterion. The best feature subset consisted of a combination of one textural and three SDT features. Using these features, an AdaBoost decision tree could predict the patient response with a cross-validated AUC0.632+ accuracy, sensitivity and specificity of 0.88, 85%, 88% and 75%, respectively. This study demonstrates, for the first time, that a combination of textural and SDT features of CT images can be used to predict breast cancer response NAC prior to the start of treatment which can potentially facilitate early therapy adjustments.
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Affiliation(s)
- Hadi Moghadas-Dastjerdi
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Shan-E-Tallat Hira Rahman
- Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Lakshmanan Sannachi
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Frances C Wright
- Surgical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, and Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Sonal Gandhi
- Division of Medical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, and Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Maureen E Trudeau
- Division of Medical Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, and Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Ali Sadeghi-Naini
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada.
| | - Gregory J Czarnota
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.
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12
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Zhu Q, Ademuyiwa FO, Young C, Appleton C, Covington MF, Ma C, Sanati S, Hagemann IS, Mostafa A, Uddin KMS, Grigsby I, Frith AE, Hernandez-Aya LF, Poplack SS. Early Assessment Window for Predicting Breast Cancer Neoadjuvant Therapy using Biomarkers, Ultrasound, and Diffuse Optical Tomography. Breast Cancer Res Treat 2021; 188:615-630. [PMID: 33970392 DOI: 10.1007/s10549-021-06239-y] [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: 01/02/2021] [Accepted: 04/20/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE The purpose of the study was to assess the utility of tumor biomarkers, ultrasound (US) and US-guided diffuse optical tomography (DOT) in early prediction of breast cancer response to neoadjuvant therapy (NAT). METHODS This prospective HIPAA compliant study was approved by the institutional review board. Forty one patients were imaged with US and US-guided DOT prior to NAT, at completion of the first three treatment cycles, and prior to definitive surgery from February 2017 to January 2020. Miller-Payne grading was used to assess pathologic response. Receiver operating characteristic curves (ROCs) were derived from logistic regression using independent variables, including: tumor biomarkers, US maximum diameter, percentage reduction of the diameter (%US), pretreatment maximum total hemoglobin concentration (HbT) and percentage reduction in HbT (%HbT) at different treatment time points. Resulting ROCs were compared using area under the curve (AUC). Statistical significance was tested using two-sided two-sample student t-test with P < 0.05 considered statistically significant. Logistic regression was used for ROC analysis. RESULTS Thirty-eight patients (mean age = 47, range 24-71 years) successfully completed the study, including 15 HER2 + of which 11 were ER + ; 12 ER + or PR + /HER2-, and 11 triple negative. The combination of HER2 and ER biomarkers, %HbT at the end of cycle 1 (EOC1) and %US (EOC1) provided the best early prediction, AUC = 0.941 (95% CI 0.869-1.0). Similarly an AUC of 0.910 (95% CI 0.810-1.0) with %US (EOC1) and %HbT (EOC1) can be achieved independent of HER2 and ER status. The most accurate prediction, AUC = 0.974 (95% CI 0.933-1.0), was achieved with %US at EOC1 and %HbT (EOC3) independent of biomarker status. CONCLUSION The combined use of tumor HER2 and ER status, US, and US-guided DOT may provide accurate prediction of NAT response as early as the completion of the first treatment cycle. CLINICAL TRIAL REGISTRATION NUMBER NCT02891681. https://clinicaltrials.gov/ct2/show/NCT02891681 , Registration time: September 7, 2016.
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Affiliation(s)
- Quing Zhu
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA. .,Washington University School of Medicine in St Louis, St. Louis, USA.
| | - Foluso O Ademuyiwa
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Catherine Young
- Washington Baylor Scott & White Health, Medical Center, Texas, Dallas, USA
| | - Catherine Appleton
- Diagnostic Imaging Associates, Ltd. St. Luke's Hospital, Chesterfield, USA
| | - Matthew F Covington
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, USA
| | - Cynthia Ma
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Souzan Sanati
- Pathology, Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Ian S Hagemann
- Washington University School of Medicine in St Louis, St. Louis, USA
| | - Atahar Mostafa
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA
| | - K M Shihab Uddin
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA
| | - Isabella Grigsby
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Ashley E Frith
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | | | - Steven S Poplack
- Washington University School of Medicine in St Louis, St. Louis, USA.,Radiology, Stanford University, Stanford, USA
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13
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Altoe ML, Kalinsky K, Marone A, Kim HK, Guo H, Hibshoosh H, Tejada M, Crew KD, Accordino MK, Trivedi MS, Hershman DL, Hielscher AH. Changes in Diffuse Optical Tomography Images During Early Stages of Neoadjuvant Chemotherapy Correlate with Tumor Response in Different Breast Cancer Subtypes. Clin Cancer Res 2021; 27:1949-1957. [PMID: 33451976 PMCID: PMC8128376 DOI: 10.1158/1078-0432.ccr-20-1108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 12/16/2020] [Accepted: 01/13/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE This study's primary objective was to evaluate the changes in optically derived parameters acquired with a diffuse optical tomography breast imaging system (DOTBIS) in the tumor volume of patients with breast carcinoma receiving neoadjuvant chemotherapy (NAC). EXPERIMENTAL DESIGN In this analysis of 105 patients with stage II-III breast cancer, normalized mean values of total hemoglobin ([Formula: see text]), oxyhemoglobin ([Formula: see text]), deoxy-hemoglobin concentration ([Formula: see text]), water, and oxygen saturation ([Formula: see text]) percentages were collected at different timepoints during NAC and compared with baseline measurements. This report compared changes in these optical biomarkers measured in patients who did not achieve a pathologic complete response (non-pCR) and those with a pCR. Differences regarding molecular subtypes were included for hormone receptor-positive and HER2-negative, HER2-positive, and triple-negative breast cancer. RESULTS At baseline, [Formula: see text] was higher for pCR tumors (3.97 ± 2.29) compared with non-pCR tumors (3.00 ± 1.72; P = 0.031). At the earliest imaging point after starting therapy, the mean change of [Formula: see text] compared with baseline ([Formula: see text]) was statistically significantly higher in non-pCR (1.23 ± 0.67) than in those with a pCR (0.87 ± 0.61; P < 0.0005), and significantly correlated to residual cancer burden classification (r = 0.448; P < 0.0005). [Formula: see text] combined with HER2 status was proposed as a two-predictor logistic model, with AUC = 0.891; P < 0.0005; and 95% confidence interval, 0.812-0.969. CONCLUSIONS This study demonstrates that DOTBIS measured features change over time according to tumor pCR status and may predict early in the NAC treatment course whether a patient is responding to NAC.
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Affiliation(s)
- Mirella L Altoe
- Departments of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York.
| | - Kevin Kalinsky
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Alessandro Marone
- Departments of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York
| | - Hyun K Kim
- Departments of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York
| | - Hua Guo
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York
| | - Hanina Hibshoosh
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York
| | - Mariella Tejada
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Katherine D Crew
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
- Department of Epidemiology, Columbia University Irving Medical Center, New York, New York
| | - Melissa K Accordino
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Meghna S Trivedi
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Dawn L Hershman
- Division of Hematology/Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
- Department of Epidemiology, Columbia University Irving Medical Center, New York, New York
| | - Andreas H Hielscher
- Departments of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York.
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14
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Osapoetra LO, Sannachi L, Quiaoit K, Dasgupta A, DiCenzo D, Fatima K, Wright F, Dinniwell R, Trudeau M, Gandhi S, Tran W, Kolios MC, Yang W, Czarnota GJ. A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods. Oncotarget 2021; 12:81-94. [PMID: 33520113 PMCID: PMC7825636 DOI: 10.18632/oncotarget.27867] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 12/29/2020] [Indexed: 12/20/2022] Open
Abstract
PURPOSE We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation. MATERIALS AND METHODS QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated. RESULTS A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy. CONCLUSIONS A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.
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Affiliation(s)
- Laurentius O Osapoetra
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Karina Quiaoit
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Daniel DiCenzo
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Kashuf Fatima
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Frances Wright
- Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Robert Dinniwell
- Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada.,Radiation Oncology, London Health Sciences Centre, London, ON, Canada.,Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Maureen Trudeau
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Sonal Gandhi
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - William Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Wei Yang
- Department of Diagnostic Radiology, University of Texas, Houston, Texas, USA
| | - Gregory J Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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15
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Jaberipour M, Sahgal A, Soliman H, Sadeghi-Naini A. Predicting Local Failure after Stereotactic Radiation Therapy in Brain Metastasis using Quantitative CT and Machine Learning .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1323-1326. [PMID: 33018232 DOI: 10.1109/embc44109.2020.9175746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Despite recent advances in cancer treatment, the prognosis of patients diagnosed with brain metastasis is still poor. The median survival is limited to months even for patients undergoing treatment. Radiation therapy is a main component of treatment for brain metastasis. However, radiotherapy cannot control local progression in up to 20% of the metastatic brain tumours. An early prediction of radiotherapy outcome for individual patients could facilitate therapy adjustments to improve its efficacy. This study investigated the potential of quantitative CT biomarkers in conjunction with machine learning methods to predict local failure after radiotherapy in brain metastasis. Volumetric CT images were acquired for radiation treatment planning from 120 patients undergoing stereotactic radiotherapy. Quantitative features characterizing the morphology and texture were extracted from different regions of each lesion. A feature reduction/selection framework was adapted to define a quantitative CT biomarker of radiotherapy outcome. Different machine learning methods were applied and evaluated to predict the local failure outcome at pre-treatment. The optimum biomarker consisting of two features in conjunction with an AdaBoost with decision tree could predict the local failure outcome with 71% accuracy on an independent test set (20 patients, 31 lesions). This study is a step forward towards prediction of radiotherapy outcome in brain metastasis using quantitative imaging and machine learning.
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16
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Quiaoit K, DiCenzo D, Fatima K, Bhardwaj D, Sannachi L, Gangeh M, Sadeghi-Naini A, Dasgupta A, Kolios MC, Trudeau M, Gandhi S, Eisen A, Wright F, Look-Hong N, Sahgal A, Stanisz G, Brezden C, Dinniwell R, Tran WT, Yang W, Curpen B, Czarnota GJ. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results. PLoS One 2020; 15:e0236182. [PMID: 32716959 PMCID: PMC7384762 DOI: 10.1371/journal.pone.0236182] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting. METHODS Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models. RESULTS Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively. CONCLUSION QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.
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Affiliation(s)
- Karina Quiaoit
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Daniel DiCenzo
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Kashuf Fatima
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Mehrdad Gangeh
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Ali Sadeghi-Naini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
| | - Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Maureen Trudeau
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Sonal Gandhi
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Andrea Eisen
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Frances Wright
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Nicole Look-Hong
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Greg Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Christine Brezden
- Department of Medical Oncology, Saint Michael's Hospital, University of Toronto, Toronto, Canada
| | - Robert Dinniwell
- Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, Canada
- Department of Radiation Oncology, London Health Sciences Centre, London, Canada
- Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - William T. Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Wei Yang
- Department of Diagnostic Radiology, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Gregory J. Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
- Department of Physics, Ryerson University, Toronto, Canada
- * E-mail:
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17
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Moghadas-Dastjerdi H, Sha-E-Tallat HR, Sannachi L, Sadeghi-Naini A, Czarnota GJ. A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning. Sci Rep 2020; 10:10936. [PMID: 32616912 PMCID: PMC7331583 DOI: 10.1038/s41598-020-67823-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 06/08/2020] [Indexed: 12/19/2022] Open
Abstract
Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early prediction of responsiveness to NAC could facilitate treatment adjustments on an individual patient basis that would be expected to improve treatment outcomes and patient survival. This study investigated, for the first time, the efficacy of quantitative computed tomography (qCT) parametric imaging to characterize intra-tumour heterogeneity and its application in predicting tumour response to NAC in LABC patients. Textural analyses were performed on CT images acquired from 72 patients before the start of chemotherapy to determine quantitative features of intra-tumour heterogeneity. The best feature subset for response prediction was selected through a sequential feature selection with bootstrap 0.632 + area under the receiver operating characteristic (ROC) curve (\documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{A}\mathrm{U}\mathrm{C}}_{0.632+}$$\end{document}AUC0.632+) as a performance criterion. Several classifiers were evaluated for response prediction using the selected feature subset. Amongst the applied classifiers an Adaboost decision tree provided the best results with cross-validated \documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{A}\mathrm{U}\mathrm{C}}_{0.632+}$$\end{document}AUC0.632+, accuracy, sensitivity and specificity of 0.89, 84%, 80% and 88%, respectively. The promising results obtained in this study demonstrate the potential of the proposed biomarkers to be used as predictors of LABC tumour response to NAC prior to the start of treatment.
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Affiliation(s)
- Hadi Moghadas-Dastjerdi
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Hira Rahman Sha-E-Tallat
- Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Lakshmanan Sannachi
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Ali Sadeghi-Naini
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada
| | - Gregory J Czarnota
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. .,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. .,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. .,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.
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18
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Moghadas-Dastjerdi H, Sha-E-Tallat HR, Sannachi L, Osapoeta LO, Sadeghi-Naini A, Czarnota GJ. Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1250-1253. [PMID: 33018214 DOI: 10.1109/embc44109.2020.9176099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Early prediction of cancer response to neoadjuvant chemotherapy (NAC) could permit personalized treatment adjustments for patients, which would improve treatment outcomes and patient survival. For the first time, the efficiency of quantitative computed tomography (qCT) textural and second derivative of textural (SDT) features were investigated and compared in this study. It was demonstrated that intra-tumour heterogeneity can be probed through these biomarkers and used as chemotherapy tumour response predictors in breast cancer patients prior to the start of treatment. These features were used to develop a machine learning approach which provided promising results with cross-validated AUC0.632+, accuracy, sensitivity and specificity of 0.86, 81%, 74% and 88%, respectively.Clinical Relevance- The results obtained in this study demonstrate the potential of textural CT biomarkers as response predictors of standard NAC before treatment initiation.
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19
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Hu Y, Lu Y, Wang S, Zhang M, Qu X, Niu B. Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs. Curr Drug Targets 2020; 20:488-500. [PMID: 30091413 DOI: 10.2174/1389450119666180809122244] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 06/19/2018] [Accepted: 06/25/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. OBJECTIVE In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. RESULTS Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. CONCLUSION This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.
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Affiliation(s)
- Yan Hu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yi Lu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Shuo Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Mengying Zhang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Xiaosheng Qu
- National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, 530023,Nanning, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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20
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Karami E, Ruschin M, Soliman H, Sahgal A, Stanisz GJ, Sadeghi-Naini A. An MR Radiomics Framework for Predicting the Outcome of Stereotactic Radiation Therapy in Brain Metastasis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1022-1025. [PMID: 31946067 DOI: 10.1109/embc.2019.8856558] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Despite recent advances in cancer treatment, patients with brain metastasis still suffer from poor overall survival (OS) after standard treatment. Predicting the treatment outcome before or early after the treatment can potentially assist the physicians in improving the therapy outcome by adjusting a standard treatment on an individual patient basis. In this study, a data-driven computational framework was proposed and investigated to predict the local control/failure (LC/LF) outcome in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The framework extracted several geometrical and textural features from the magnetic resonance (MR) images of the tumour and edema regions acquired for 38 patients. Subsequent to a multi-step feature reduction/selection, a quantitative MR biomarker consisting of two features was constructed. A support vector machine classifier was used for outcome prediction using the constructed MR biomarker. The bootstrap .632+ and leave-one-patient-out cross-validation methods were used to assess the model's performance. The results indicated that the outcome of LF after SRT could be predicted with an area under the curve of 0.80 and a cross-validated accuracy of 82%. The results obtained implied a good potential of the proposed framework for local outcome prediction in patients with brain metastasis treated with SRT and encourage further investigations on a larger cohort of patients.
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21
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Karami E, Jalalifar A, Ruschin M, Soliman H, Sahgal A, Stanisz GJ, Sadeghi-Naini A. An Automatic Framework for Segmentation of Brain Tumours at Follow-up Scans after Radiation Therapy .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:463-466. [PMID: 31945938 DOI: 10.1109/embc.2019.8856858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain metastasis is the most common intracranial malignancy with a poor overall survival (OS) after treatment. The standard stereotactic radiation therapy (SRT) planning procedure for brain metastasis requires delineating the tumour volume on magnetic resonance (MR) images. MR images are also acquired at multiple follow-up scans after SRT to monitor the treatment outcome through measuring changes in the physical dimensions of the tumour. Such measurements require manual segmentation of the tumour volume on multiple slices of several follow-up images which is tedious and impedes the SRT evaluation work flow considerably. In this study, an automatic framework was proposed to segment the tumour volume on longitudinal MR images acquired at standard follow-up scans after SRT. The multi-step segmentation framework was based on region growing and morphological snakes models that applied the standard SRT planning tumour contour as a basis to approximate the tumour shape and location at each follow-up scan for an accurate automatic segmentation of tumour volume. The framework was evaluated using the MR imaging data acquired from five patients prior to and at three follow-up scans after SRT. The preliminary results indicated that the Dice similarity coefficient between the ground truth tumour masks and their automatically segmented counterparts ranged between 0.84 and 0.90, while the average Dice coefficient for all the follow-up scans was 0.88. The results obtained implied a good potential of the proposed framework for being incorporated into the SRT treatment planning and evaluation systems as well as outcome prediction models.
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22
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Karami E, Soliman H, Ruschin M, Sahgal A, Myrehaug S, Tseng CL, Czarnota GJ, Jabehdar-Maralani P, Chugh B, Lau A, Stanisz GJ, Sadeghi-Naini A. Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis. Sci Rep 2019; 9:19830. [PMID: 31882597 PMCID: PMC6934477 DOI: 10.1038/s41598-019-56185-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 12/08/2019] [Indexed: 02/08/2023] Open
Abstract
About 20–40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.
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Affiliation(s)
- Elham Karami
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Gregory J Czarnota
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | | | - Brige Chugh
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Angus Lau
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Greg J Stanisz
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Neurosurgery and Pediatric Neurosurgery, Medical University, Lublin, Poland
| | - Ali Sadeghi-Naini
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. .,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. .,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
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23
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Makarov NS, Ramasamy K, Jackson A, Velarde A, Castaneda C, Archuleta N, Hebert D, Bergren MR, McDaniel H. Fiber-Coupled Luminescent Concentrators for Medical Diagnostics, Agriculture, and Telecommunications. ACS NANO 2019; 13:9112-9121. [PMID: 31291097 DOI: 10.1021/acsnano.9b03335] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
While luminescent concentrators (LCs) are mainly designed to harvest sunlight and convert its energy into electricity, the same concept can be advantageous in alternative applications. Examples of such applications are demonstrated here by coupling the edge-guided light of high-performance LCs based on CuInSexS2-x/ZnS quantum dots into optical fibers with emission covering visible-to-NIR spectral regions. In particular, a cost-efficient, miniature broadband light source for medical diagnostics, a spectral-conversion and light-guiding device for agriculture, and a large-area broadband tunable detector for telecommunications are demonstrated. Various design considerations and performance optimization approaches are discussed and summarized. Prototypes of the devices are manufactured and tested. Individual elements of the broadband light source show coupling efficiencies up to 1%, which is sufficient to saturate typical fiber-coupled spectrometers at a minimal integration time of 1 ms using 100 mW blue excitation. Agricultural devices are capable of delivering ∼10% of photosynthetically active radiation (per device) converted from absorbed sunlight to the lower canopy of plants, which boosted the tomato yield in a commercial greenhouse by 7% (fresh weight). Finally, large-scale prototype detectors can be used to discern time-modulated unfocused signals with an average power as low as 1 μW, which would be useful for free-space telecommunication systems. Fully optimized devices are expected to make significant impacts on speed and bandwidth of free-space telecommunication systems, medical diagnostics, and greenhouse crop yields.
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Affiliation(s)
| | | | - Aaron Jackson
- UbiQD, Inc. , Los Alamos , New Mexico 87544 , United States
| | - Andres Velarde
- UbiQD, Inc. , Los Alamos , New Mexico 87544 , United States
| | | | - Nic Archuleta
- UbiQD, Inc. , Los Alamos , New Mexico 87544 , United States
| | - Damon Hebert
- UbiQD, Inc. , Los Alamos , New Mexico 87544 , United States
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24
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Tadayyon H, Gangeh M, Sannachi L, Trudeau M, Pritchard K, Ghandi S, Eisen A, Look-Hong N, Holloway C, Wright F, Rakovitch E, Vesprini D, Tran WT, Curpen B, Czarnota G. A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks. Oncotarget 2019; 10:3910-3923. [PMID: 31231468 PMCID: PMC6570472 DOI: 10.18632/oncotarget.26996] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 05/13/2019] [Indexed: 11/25/2022] Open
Abstract
We demonstrate the clinical utility of combining quantitative ultrasound (QUS) imaging of the breast with an artificial neural network (ANN) classifier to predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) administration prior to the start of treatment. Using a 6 MHz ultrasound system, radiofrequency (RF) ultrasound data were acquired from 100 patients with biopsy-confirmed locally advanced breast cancer prior to the start of NAC. Quantitative ultrasound mean parameter intensity and texture features were computed from the tumour core and margin, and were compared to the clinical/pathological response and 5-year recurrence-free survival (RFS) of patients. A multi-parametric QUS model in conjunction with an ANN classifier predicted patient response with 96 ± 6% accuracy, and a 0.96 ± 0.08 area under the receiver operating characteristic curve (AUC), compared to 65 ± 10 % accuracy and 0.67 ± 0.14 AUC achieved using a K-Nearest Neighbour (KNN) algorithm. A separate ANN model predicted patient RFS with 85 ± 7% accuracy, and a 0.89 ± 0.11 AUC, whereas the KNN methodology achieved a 58 ± 6 % accuracy and a 0.64 ± 0.09 AUC. The application of ANN for classifying patient response based on tumour QUS features performs well in terms of predicting response to chemotherapy. The findings here provide a framework for developing personalized a priori chemotherapy selection for patients that are candidates for NAC, potentially resulting in improved patient treatment outcomes and prognosis.
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Affiliation(s)
- Hadi Tadayyon
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mehrdad Gangeh
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Maureen Trudeau
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Kathleen Pritchard
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Sonal Ghandi
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Andrea Eisen
- Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Nicole Look-Hong
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Claire Holloway
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Frances Wright
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Eileen Rakovitch
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Danny Vesprini
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - William Tyler Tran
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, and Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Gregory Czarnota
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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25
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Liu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, Sun K, Tang Z, Jiang H, Li H, Xiong Q, Ding Y, Zhao X, Wang K, Liu Z, Tian J. Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study. Clin Cancer Res 2019; 25:3538-3547. [PMID: 30842125 DOI: 10.1158/1078-0432.ccr-18-3190] [Citation(s) in RCA: 256] [Impact Index Per Article: 51.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 12/16/2018] [Accepted: 03/05/2019] [Indexed: 01/06/2023]
Abstract
PURPOSE We evaluated the performance of the newly proposed radiomics of multiparametric MRI (RMM), developed and validated based on a multicenter dataset adopting a radiomic strategy, for pretreatment prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. EXPERIMENTAL DESIGN A total of 586 potentially eligible patients were retrospectively enrolled from four hospitals (primary cohort and external validation cohort 1-3). Quantitative imaging features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging before NAC for each patient. With features selected using a coarse to fine feature selection strategy, four radiomic signatures were constructed based on each of the three MRI sequences and their combination. RMM was developed based on the best radiomic signature incorporating with independent clinicopathologic risk factors. The performance of RMM was assessed with respect to its discrimination and clinical usefulness, and compared with that of clinical information-based prediction model. RESULTS Radiomic signature combining multiparametric MRI achieved an AUC of 0.79 (the highest among the four radiomic signatures). The signature further achieved good performances in hormone receptor-positive and HER2-negative group and triple-negative group. RMM yielded an AUC of 0.86, which was significantly higher than that of clinical model in two of the three external validation cohorts. CONCLUSIONS The study suggested a possibility that RMM provided a potential tool to develop a model for predicting pCR to NAC in breast cancer.
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Affiliation(s)
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhuolin Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, Yunnan, China
| | - Jinrong Qu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Renzhi Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuezhi Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Longfei Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, China
| | - Kai Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Zhenchao Tang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Hui Jiang
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Hailiang Li
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Qianqian Xiong
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yingying Ding
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, Yunnan, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
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26
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Tang Z, Zhang XY, Liu Z, Li XT, Shi YJ, Wang S, Fang M, Shen C, Dong E, Sun YS, Tian J. Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer. Radiother Oncol 2019; 132:100-108. [DOI: 10.1016/j.radonc.2018.11.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 10/16/2018] [Accepted: 11/13/2018] [Indexed: 12/14/2022]
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Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 2019; 9:1303-1322. [PMID: 30867832 PMCID: PMC6401507 DOI: 10.7150/thno.30309] [Citation(s) in RCA: 447] [Impact Index Per Article: 89.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 01/10/2019] [Indexed: 12/14/2022] Open
Abstract
Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.
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Affiliation(s)
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Cheng Fang
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Xuezhi Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Kai Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Longfei Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
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28
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Liu YH, Xue LB, Yang YF, Zhao TJ, Bai Y, Zhang BY, Li J. Diffuse optical spectroscopy for monitoring the responses of patients with breast cancer to neoadjuvant chemotherapy: A meta-analysis. Medicine (Baltimore) 2018; 97:e12683. [PMID: 30313063 PMCID: PMC6203577 DOI: 10.1097/md.0000000000012683] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND This study aimed to investigate the potential of diffuse optical spectroscopy (DOT) for monitoring the responses of patients with breast cancer to neoadjuvant chemotherapy (NAC). METHODS We searched PubMed, EMBASE, Cochrane Database of Systematic Reviews, and Web of Science for relevant studies. Data were extracted for pooled analysis, heterogeneity testing, threshold effect testing, sensitivity analysis, publication bias analysis, and subgroup analysis. RESULTS The pooled meta-analysis of the 10 eligible studies that included 422 patients indicated the high performance of DOT for monitoring total patient responses to NAC (OR = 14.78, 95% CI: 8.23-26.54, P < .001), with low significant heterogeneity (I = 7.2%, P = .375). DOT possessed an area under the curve of 0.84 (95% CI: 0.81-0.87) to distinguish total patient responses to NAC. Subgroup analysis showed that the pooled sensitivity of DOT for monitoring pathologic complete response to NAC was 87%, and the pooled specificity was 70%. Meanwhile, the pooled sensitivity of DOT for monitoring pathologic complete and partial responses to NAC was 82%, and the pooled specificity was 82%. Although Begg's funnel plot (P = .049) indicated the presence of publication bias among the included studies, trim-and-fill method verified the stability of the pooled outcomes. CONCLUSION Our meta-analysis of available published data indicated that DOT can be potentially used to predict and monitor patient responses to NAC. A larger study population is needed to fully assess the use of DOT for guiding therapies and predicting responses of individual subjects to NAC.
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Affiliation(s)
| | | | - Yan Fang Yang
- Anesthesiology Department, Cangzhou Central Hospital, Yunhe Qu, Cangzhou City
| | - Tian Jiao Zhao
- General Surgery, You Fu Hospital, Xinhua Qu, Shijiazhuang City, China
| | | | | | - Jie Li
- Thyroid and Breast Surgery
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29
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Yu YH, Zhu X, Mo QG, Cui Y. Prediction of neoadjuvant chemotherapy response using diffuse optical spectroscopy in breast cancer. Clin Transl Oncol 2017; 20:524-533. [PMID: 28921461 PMCID: PMC5978895 DOI: 10.1007/s12094-017-1745-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 08/20/2017] [Indexed: 12/19/2022]
Abstract
PURPOSE Near-infrared diffuse optical spectroscopy (DOS) has been recently used to predict neoadjuvant chemotherapy response (NAC). In the present study, we explore the change in blood-oxygen content using DOS to predict NAC response against breast cancer. MATERIALS AND METHODS A total of 20 patients were enrolled and underwent DOS scan with blood-oxygen detection before each treatment cycle. The first DOS scan was performed before NAC treatment (pretreatment), and subsequent scans were performed after each NAC treatment circle. Changes in blood content and oxygen content by DOS were evaluated and compared with tumor size, and their changes were analyzed in response versus nonresponse group. RESULTS Thirteen patients were classified into response and seven patients into nonresponse group. The tumor blood content value (-1.06 ± 0.43) and oxygen content value (0.48 ± 0.17) of DOS at pretreatment was significantly different from presurgery in response group (P < 0.05), but not in nonresponse group. In response group, the percentage change in blood content (median 91.19%) was significantly larger than tumor size (median 48.89%) (P = 0.0035), while in oxygen content (median 47.11%) is not (P = 0.2815). Comparing each cycle, the percentage change in blood content could distinguish responder from non-responder as early as after the first treatment cycle (19.1 versus 6.6%, P = 0.0265). Blood content percentage sensitivity was 76.9% and specificity was 85.7% (AUC 0.912), while oxygen content percentage sensitivity was 76.9% and specificity was 71.4% (AUC 0.797). CONCLUSION Both blood and oxygen content measured by DOS could be used to discriminate responder to the treatment versus non-responder. Among the two, percentage change of blood content was more precise and earlier than that of oxygen content to predicted breast tumor response. The percentage change in blood content could distinguish responder from non-responder after the first treatment cycle.
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Affiliation(s)
- Ying-Hua Yu
- Department of Breast Surgery, The Affiliated Tumor Hospital of Guangxi Medical University, NO. 71, He Di Lu, Nanning, Guangxi, 530021, People's Republic of China
| | - Xiao Zhu
- Department of Breast Surgery, The Affiliated Tumor Hospital of Guangxi Medical University, NO. 71, He Di Lu, Nanning, Guangxi, 530021, People's Republic of China
| | - Qin-Guo Mo
- Department of Breast Surgery, The Affiliated Tumor Hospital of Guangxi Medical University, NO. 71, He Di Lu, Nanning, Guangxi, 530021, People's Republic of China.
| | - Ying Cui
- The Graduate School, The Affiliated Tumor Hospital of Guangxi Medical University, NO. 71, He Di Lu, Nanning, Guangxi, 530021, People's Republic of China.
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