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Harada TL, Uematsu T, Nakashima K, Sugino T, Nishimura S, Takahashi K, Hayashi T, Tadokoro Y. Non-contrast-enhanced breast MRI for evaluation of tumor volume change after neoadjuvant chemotherapy. Eur J Radiol 2024; 177:111555. [PMID: 38880053 DOI: 10.1016/j.ejrad.2024.111555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 05/06/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024]
Abstract
PURPOSE Three-dimensional contrast-enhanced magnetic resonance imaging (3D-Ce-MRI) is a most powerful tool for evaluation of neoadjuvant chemotherapy (NAC). However, the use of contrast agent is invasive, expensive, and time consuming, Thus, contrast agent-free imaging is preferable. We aimed to investigate the tumor volume change after NAC using maximum intensity projection diffusion-weighted image (MIP-DWI) and 3D-Ce-MRI. METHOD We finally enrolled 55 breast cancer patients who underwent NAC in 2018. All MRI analyses were performed using SYNAPSE VINCENT® medical imaging system (Fujifilm Medical, Tokyo, Japan). We evaluated the tumor volumes before, during, and after NAC. Tumor volume before NAC on 3D-Ce-MRI was termed Pre-CE and those during and after NAC were termed Post-CE. The observer raised the lower end of the window width until the tumor was clearly visible and then manually deleted the non-tumor tissues. A month thereafter, the same observer who was blinded to the 3D-Ce-MRI results randomly evaluated the tumor volumes (Pre-DWI and Post-DWI) using MIP-DWI with the same method. Tumor volume change between ΔCE (Pre-CE - Post-CE/Pre-CE) and ΔDWI (Pre-DWI - Post-DWI/Pre-DWI) and the processing time for both methods (Time-DWI and Time-CE) were compared. RESULTS We enrolled 55 patients. Spearman's rho between ΔDWI and ΔCE for pure mass lesions, and non-mass enhancement (NME) was 0.89 (p < 0.01), 0.63(p < 0.01) respectively. Time-DWI was significantly shorter than Time-CE (41.3 ± 21.2 and 199.5 ± 98.3 respectively, p < 0.01). CONCLUSIONS Non-contrast-enhanced Breast MRI enables appropriate and faster evaluation of tumor volume change after NAC than 3D-Ce-MRI especially for mass lesions.
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Affiliation(s)
- Taiyo L Harada
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Takayoshi Uematsu
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan.
| | - Kazuaki Nakashima
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Takashi Sugino
- Division of Pathology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Seiichirou Nishimura
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Kaoru Takahashi
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Tomomi Hayashi
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Yukiko Tadokoro
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
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Qadir A, Singh N, Moe AAK, Cahoon G, Lye J, Chao M, Foroudi F, Uribe S. Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review. Clin Breast Cancer 2024:S1526-8209(24)00136-8. [PMID: 38906720 DOI: 10.1016/j.clbc.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/07/2024] [Accepted: 05/26/2024] [Indexed: 06/23/2024]
Abstract
The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.
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Affiliation(s)
- Ayyaz Qadir
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia.
| | - Nabita Singh
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
| | - Aung Aung Kywe Moe
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
| | - Glenn Cahoon
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Jessica Lye
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Michael Chao
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia; Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Farshad Foroudi
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia; Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Heidelberg, Victoria, Australia
| | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Monash University, Melbourne, Australia
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3
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Abbasi AB, Wu V, Lang JE, Esserman LJ. Precision Oncology in Breast Cancer Surgery. Surg Oncol Clin N Am 2024; 33:293-310. [PMID: 38401911 DOI: 10.1016/j.soc.2023.12.011] [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] [Indexed: 02/26/2024]
Abstract
Outcomes for patients with breast cancer have improved over time due to increased screening and the availability of more effective therapies. It is important to recognize that breast cancer is a heterogeneous disease that requires treatment based on molecular characteristics. Early endpoints such as pathologic complete response correlate with event-free survival, allowing the opportunity to consider de-escalation of certain cancer treatments to avoid overtreatment. This article discusses clinical trials of tailoring treatment (eg, I-SPY2) and screening (eg, WISDOM) to individual patients based on their unique risk features.
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Affiliation(s)
- Ali Benjamin Abbasi
- Department of Surgery, San Francisco Breast Care Center, University of California, Box 1710, UCSF, San Francisco, CA 94143, USA
| | - Vincent Wu
- Department of Surgery, Cleveland Clinic Breast Services, 9500 Euclid Avenue, A80, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Julie E Lang
- Department of Surgery, Cleveland Clinic Breast Services, 9500 Euclid Avenue, A80, Cleveland Clinic, Cleveland, OH 44195, USA.
| | - Laura J Esserman
- Department of Surgery, San Francisco Breast Care Center, University of California, Box 1710, UCSF, San Francisco, CA 94143, USA
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4
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Diwanji D, Onishi N, Hathi DK, Lawhn-Heath C, Kornak J, Li W, Guo R, Molina-Vega J, Seo Y, Flavell RR, Heditsian D, Brain S, Esserman LJ, Joe BN, Hylton NM, Jones EF, Ray KM. 18F-FDG Dedicated Breast PET Complementary to Breast MRI for Evaluating Early Response to Neoadjuvant Chemotherapy. Radiol Imaging Cancer 2024; 6:e230082. [PMID: 38551406 PMCID: PMC10988337 DOI: 10.1148/rycan.230082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/30/2023] [Accepted: 02/16/2024] [Indexed: 04/02/2024]
Abstract
Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Devan Diwanji
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Natsuko Onishi
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Deep K. Hathi
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Courtney Lawhn-Heath
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - John Kornak
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Wen Li
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Ruby Guo
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Julissa Molina-Vega
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Youngho Seo
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Robert R. Flavell
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Diane Heditsian
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Susie Brain
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Laura J. Esserman
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Bonnie N. Joe
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Nola M. Hylton
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Ella F. Jones
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
| | - Kimberly M. Ray
- From the Departments of Radiology and Biomedical Imaging (D.D., N.O.,
D.K.H., C.L.H., W.L., R.G., Y.S., R.R.F., B.N.J., N.M.H., E.F.J., K.M.R.),
Epidemiology and Biostatistics (J.K.), and Surgery (J.M.V., L.J.E.), University
of California San Francisco, 550 16th St, San Francisco, CA 94158; and
I-SPY 2 Advocacy Group, San Francisco, Calif (D.H., S.B.)
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5
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Musall BC, Rauch DE, Mohamed RM, Panthi B, Boge M, Candelaria RP, Chen H, Guirguis MS, Hunt KK, Huo L, Hwang KP, Korkut A, Litton JK, Moseley TW, Pashapoor S, Patel MM, Reed BJ, Scoggins ME, Son JB, Tripathy D, Valero V, Wei P, White JB, Whitman GJ, Xu Z, Yang WT, Yam C, Adrada BE, Ma J. Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy. J Magn Reson Imaging 2024:10.1002/jmri.29267. [PMID: 38294179 PMCID: PMC11289164 DOI: 10.1002/jmri.29267] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/18/2024] [Accepted: 01/18/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). PURPOSE To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. STUDY TYPE Prospective. POPULATION Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). FIELD STRENGTH/SEQUENCE 3.0 T/reduced field of view single-shot echo-planar DTI sequence. ASSESSMENT Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. STATISTICAL TESTS Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. RESULTS 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s). DATA CONCLUSION Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Benjamin C. Musall
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David E. Rauch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rania M.M. Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rosalind P. Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mary S. Guirguis
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kelly K. Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Anil Korkut
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer K. Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tanya W. Moseley
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Miral M. Patel
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brandy J. Reed
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Marion E. Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason B. White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gary J. Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Wei T. Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beatriz E. Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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6
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Peterson JR, Cole JA, Pfeiffer JR, Norris GH, Zhang Y, Lopez-Ramos D, Pandey T, Biancalana M, Esslinger HR, Antony AK, Takiar V. Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer. Breast Cancer Res 2023; 25:54. [PMID: 37165441 PMCID: PMC10170712 DOI: 10.1186/s13058-023-01654-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform. METHODS A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction). RESULTS The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054). CONCLUSION We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.
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Affiliation(s)
- Joseph R Peterson
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
| | - John A Cole
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA
| | - John R Pfeiffer
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA
| | - Gregory H Norris
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA
| | - Yuhan Zhang
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA
| | - Dorys Lopez-Ramos
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA
| | - Tushar Pandey
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA
| | | | - Hope R Esslinger
- Department of Radiation Oncology, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - Anuja K Antony
- SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA
| | - Vinita Takiar
- Department of Radiation Oncology, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
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Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI. Cancers (Basel) 2023; 15:cancers15041025. [PMID: 36831368 PMCID: PMC9953797 DOI: 10.3390/cancers15041025] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 02/08/2023] Open
Abstract
Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.
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8
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Rahmat K, Mumin NA, Hamid MTR, Hamid SA, Ng WL. MRI Breast: Current Imaging Trends, Clinical Applications, and Future Research Directions. Curr Med Imaging 2022; 18:1347-1361. [PMID: 35430976 DOI: 10.2174/1573405618666220415130131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/11/2022] [Accepted: 03/02/2022] [Indexed: 01/25/2023]
Abstract
Magnetic Resonance Imaging (MRI) is the most sensitive and advanced imaging technique in diagnosing breast cancer and is essential in improving cancer detection, lesion characterization, and determining therapy response. In addition to the dynamic contrast-enhanced (DCE) technique, functional techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) further characterize and differentiate benign and malignant lesions thus, improving diagnostic accuracy. There is now an increasing clinical usage of MRI breast, including screening in high risk and supplementary screening tools in average-risk patients. MRI is becoming imperative in assisting breast surgeons in planning breast-conserving surgery for preoperative local staging and evaluation of neoadjuvant chemotherapy response. Other clinical applications for MRI breast include occult breast cancer detection, investigation of nipple discharge, and breast implant assessment. There is now an abundance of research publications on MRI Breast with several areas that still remain to be explored. This review gives a comprehensive overview of the clinical trends of MRI breast with emphasis on imaging features and interpretation using conventional and advanced techniques. In addition, future research areas in MRI breast include developing techniques to make MRI more accessible and costeffective for screening. The abbreviated MRI breast procedure and an area of focused research in the enhancement of radiologists' work with artificial intelligence have high impact for the future in MRI Breast.
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Affiliation(s)
- Kartini Rahmat
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, Faculty of Medicine, Kuala Lumpur, Malaysia
| | - Nazimah Ab Mumin
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Marlina Tanty Ramli Hamid
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Shamsiah Abdul Hamid
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Wei Lin Ng
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, Faculty of Medicine, Kuala Lumpur, Malaysia
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9
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Evaluation of Breast Cancer Size Measurement by Computer-Aided Diagnosis (CAD) and a Radiologist on Breast MRI. J Clin Med 2022; 11:jcm11051172. [PMID: 35268263 PMCID: PMC8911102 DOI: 10.3390/jcm11051172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/19/2022] [Accepted: 02/21/2022] [Indexed: 02/05/2023] Open
Abstract
Purpose: This study aimed to evaluate cancer size measurement by computer-aided diagnosis (CAD) and radiologist on breast magnetic resonance imaging (MRI) relative to histopathology and to determine clinicopathologic and MRI factors that may affect measurements. Methods: Preoperative MRI of 208 breast cancers taken between January 2017 and March 2021 were included. We evaluated correlation between CAD-generated size and pathologic size as well as that between radiologist-measured size and pathologic size. We classified size discrepancies into accurate and inaccurate groups. For both CAD and radiologist, clinicopathologic and imaging factors were compared between accurate and inaccurate groups. Results: The mean sizes as predicted by CAD, radiologist and pathology were 2.66 ± 1.68 cm, 2.54 ± 1.68 cm, and 2.30 ± 1.61 cm, with significant difference (p < 0.001). Correlation coefficients of cancer size measurement by radiologist and CAD in reference to pathology were 0.898 and 0.823. Radiologist’s measurement was more accurate than CAD, with statistical significance (p < 0.001). CAD-generated measurement was significantly more inaccurate for cancers of larger pathologic size (>2 cm), in the presence of an extensive intraductal component (EIC), with positive progesterone receptor (PR), and of non-mass enhancement (p = 0.045, 0.045, 0.03 and 0.002). Radiologist-measured size was significantly more inaccurate for cancers in presence of an in situ component, EIC, positive human epidermal growth factor receptor 2 (HER2), and non-mass enhancement (p = 0.017, 0.008, 0.003 and <0.001). Conclusion: Breast cancer size measurement showed a very strong correlation between CAD and pathology and radiologist and pathology. Radiologist-measured size was significantly more accurate than CAD size. Cancer size measurement by CAD and radiologist can both be inaccurate for cancers with EIC or non-mass enhancement.
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10
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Kirkham AA, King K, Joy AA, Pelletier AB, Mackey JR, Young K, Zhu X, Meza-Junco J, Basi SK, Hiller JP, Brkin T, Michalowski B, Pituskin E, Paterson DI, Courneya KS, Thompson RB, Prado CM. Rationale and design of the Diet Restriction and Exercise-induced Adaptations in Metastatic breast cancer (DREAM) study: a 2-arm, parallel-group, phase II, randomized control trial of a short-term, calorie-restricted, and ketogenic diet plus exercise during intravenous chemotherapy versus usual care. BMC Cancer 2021; 21:1093. [PMID: 34629067 PMCID: PMC8504029 DOI: 10.1186/s12885-021-08808-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND An underlying cause of solid tumor resistance to chemotherapy treatment is diminished tumor blood supply, which leads to a hypoxic microenvironment, dependence on anaerobic energy metabolism, and impaired delivery of intravenous treatments. Preclinical data suggest that dietary strategies of caloric restriction and low-carbohydrate intake can inhibit glycolysis, while acute exercise can transiently enhance blood flow to the tumor and reduce hypoxia. The Diet Restriction and Exercise-induced Adaptations in Metastatic Breast Cancer (DREAM) study will compare the effects of a short-term, 50% calorie-restricted and ketogenic diet combined with aerobic exercise performed during intravenous chemotherapy treatment to usual care on changes in tumor burden, treatment side effects, and quality of life. METHODS Fifty patients with measurable metastases and primary breast cancer starting a new line of intravenous chemotherapy will be randomly assigned to usual care or the combined diet and exercise intervention. Participants assigned to the intervention group will be provided with food consisting of 50% of measured calorie needs with 80% of calories from fat and ≤ 10% from carbohydrates for 48-72 h prior to each chemotherapy treatment and will perform 30-60 min of moderate-intensity cycle ergometer exercise during each chemotherapy infusion, for up to six treatment cycles. The diet and exercise durations will be adapted for each chemotherapy protocol. Tumor burden will be assessed by change in target lesion size using axial computed tomography (primary outcome) and magnetic resonance imaging (MRI)-derived apparent diffusion coefficient (secondary outcome) after up to six treatments. Tertiary outcomes will include quantitative MRI markers of treatment toxicity to the heart, thigh skeletal muscle, and liver, and patient-reported symptoms and quality of life. Exploratory outcome measures include progression-free and overall survival. DISCUSSION The DREAM study will test a novel, short-term diet and exercise intervention that is targeted to mechanisms of tumor resistance to chemotherapy. A reduction in lesion size is likely to translate to improved cancer outcomes including disease progression and overall survival. Furthermore, a lifestyle intervention may empower patients with metastatic breast cancer by actively engaging them to play a key role in their treatment. TRIAL REGISTRATION ClinicalTrials.gov, NCT03795493 , registered 7 January, 2019.
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Affiliation(s)
- Amy A Kirkham
- Faculty of Kinesiology & Physical Education, University of Toronto, 422, 100 Devonshire Pl, Toronto, ON, M5S 2C9, Canada.
| | - Karen King
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Anil A Joy
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | | | - John R Mackey
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Kelvin Young
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Xiaofu Zhu
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Judith Meza-Junco
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Sanraj K Basi
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Julie Price Hiller
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Tina Brkin
- Cross Cancer Institute, Edmonton, AB, Canada
| | | | - Edith Pituskin
- Cross Cancer Institute, Edmonton, AB, Canada
- University of Alberta, Edmonton, AB, Canada
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Comes MC, La Forgia D, Didonna V, Fanizzi A, Giotta F, Latorre A, Martinelli E, Mencattini A, Paradiso AV, Tamborra P, Terenzio A, Zito A, Lorusso V, Massafra R. Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs. Cancers (Basel) 2021; 13:2298. [PMID: 34064923 PMCID: PMC8151784 DOI: 10.3390/cancers13102298] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/05/2021] [Accepted: 05/08/2021] [Indexed: 12/12/2022] Open
Abstract
Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR.
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Affiliation(s)
- Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Agnese Latorre
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Eugenio Martinelli
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133 Rome, Italy; (E.M.); (A.M.)
- Dipartimento di Ingegneria Elettronica, Università di Roma Tor Vergata, Via del Politecnico 1, 00133 Roma, Italy
| | - Arianna Mencattini
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133 Rome, Italy; (E.M.); (A.M.)
- Dipartimento di Ingegneria Elettronica, Università di Roma Tor Vergata, Via del Politecnico 1, 00133 Roma, Italy
| | - Angelo Virgilio Paradiso
- Oncologia Medica Sperimentale, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Antonella Terenzio
- Unità di Oncologia Medica, Università Campus Bio-Medico, 00128 Roma, Italy;
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
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Adrada BE, Candelaria R, Moulder S, Thompson A, Wei P, Whitman GJ, Valero V, Litton JK, Santiago L, Scoggins ME, Moseley TW, White JB, Ravenberg EE, Yang WT, Rauch GM. Early ultrasound evaluation identifies excellent responders to neoadjuvant systemic therapy among patients with triple-negative breast cancer. Cancer 2021; 127:2880-2887. [PMID: 33878210 DOI: 10.1002/cncr.33604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/06/2021] [Accepted: 03/18/2021] [Indexed: 01/12/2023]
Abstract
BACKGROUND Heterogeneity exists in the response of triple-negative breast cancer (TNBC) to standard anthracycline (AC)/taxane-based neoadjuvant systemic therapy (NAST), with 40% to 50% of patients having a pathologic complete response (pCR) to therapy. Early assessment of the imaging response during NAST may identify a subset of TNBCs that are likely to have a pCR upon completion of treatment. The authors aimed to evaluate the performance of early ultrasound (US) after 2 cycles of neoadjuvant NAST in identifying excellent responders to NAST among patients with TNBC. METHODS Two hundred fifteen patients with TNBC were enrolled in the ongoing ARTEMIS (A Robust TNBC Evaluation Framework to Improve Survival) clinical trial. The patients were divided into a discovery cohort (n = 107) and a validation cohort (n = 108). A receiver operating characteristic analysis with 95% confidence intervals (CIs) and a multivariate logistic regression analysis were performed to model the probability of a pCR on the basis of the tumor volume reduction (TVR) percentage by US from the baseline to after 2 cycles of AC. RESULTS Overall, 39.3% of the patients (42 of 107) achieved a pCR. A positive predictive value (PPV) analysis identified a cutoff point of 80% TVR after 2 cycles; the pCR rate was 77% (17 of 22) in patients with a TVR ≥ 80%, and the area under the curve (AUC) was 0.84 (95% CI, 0.77-0.92; P < .0001). In the validation cohort, the pCR rate was 44%. The PPV for pCR with a TVR ≥ 80% after 2 cycles was 76% (95% CI, 55%-91%), and the AUC was 0.79 (95% CI, 0.70-0.87; P < .0001). CONCLUSIONS The TVR percentage by US evaluation after 2 cycles of NAST may be a cost-effective early imaging biomarker for a pCR to AC/taxane-based NAST.
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Affiliation(s)
- Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rosalind Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Stacy Moulder
- Department of Breast Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alastair Thompson
- Department of Breast Surgery, University of Baylor College of Medicine, Houston, Texas.,Lester and Sue Smith Breast Cancer, University of Baylor College of Medicine, Houston, Texas
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lumarie Santiago
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tanya W Moseley
- Department of Breast Imaging and Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Elizabeth E Ravenberg
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Applegate MB, Amelard R, Gomez CA, Roblyer D. Real-Time Handheld Probe Tracking and Image Formation Using Digital Frequency-Domain Diffuse Optical Spectroscopy. IEEE Trans Biomed Eng 2021; 68:3399-3409. [PMID: 33835913 DOI: 10.1109/tbme.2021.3072036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Frequency-domain diffuse optical spectroscopic imaging (FD-DOS) is a non-invasive method for measuring absolute concentrations of tissue chromophores such as oxy- and deoxy-hemoglobin in vivo. The utility of FD-DOS for clinical applications such as monitoring chemotherapy response in breast cancer has previously been demonstrated, but challenges for further clinical translation, such as slow acquisition speed and lack of user feedback, remain. Here, we propose a new high speed FD-DOS instrument that allows users to freely acquire measurements over the tissue surface, and is capable of rapidly imaging large volumes of tissue. METHODS We utilize 3D monocular probe tracking combined with custom digital FD-DOS hardware and a high-speed data processing pipeline for the instrument. Results are displayed during scanning over the surface of the sample using a probabilistic Monte Carlo light propagation model. RESULTS We show this instrument can measure absorption and scattering coefficients with an error of 7% and 1% respectively, with 0.7 mm positional accuracy. We demonstrate the equivalence of our visualization methodology with a standard interpolation approach, and demonstrate two proof-of-concept in vivo results showing superficial vasculature in the human forearm and surface contrast in a healthy human breast. CONCLUSION Our new FD-DOS system is able to compute chromophore concentrations in real-time (1.5 Hz) in vivo. SIGNIFICANCE This method has the potential to improve the quality of FD-DOS image scans while reducing measurement times for a variety of clinical applications.
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Alzu’bi A, Najadat H, Doulat W, Al-Shari O, Zhou L. Predicting the recurrence of breast cancer using machine learning algorithms. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:13787-13800. [DOI: 10.1007/s11042-020-10448-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 08/24/2020] [Accepted: 12/22/2020] [Indexed: 08/29/2023]
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15
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Musall BC, Abdelhafez AH, Adrada BE, Candelaria RP, Mohamed RMM, Boge M, Le-Petross H, Arribas E, Lane DL, Spak DA, Leung JWT, Hwang KP, Son JB, Elshafeey NA, Mahmoud HS, Wei P, Sun J, Zhang S, White JB, Ravenberg EE, Litton JK, Damodaran S, Thompson AM, Moulder SL, Yang WT, Pagel MD, Rauch GM, Ma J. Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer. J Magn Reson Imaging 2021; 54:251-260. [PMID: 33586845 DOI: 10.1002/jmri.27557] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response. PURPOSE To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). STUDY TYPE Prospective. POPULATION/SUBJECTS Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020. FIELD STRENGTH/SEQUENCE A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing. STATISTICAL TESTS Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test. RESULTS About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05). DATA CONCLUSION FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: 4.
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Affiliation(s)
- Benjamin C Musall
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abeer H Abdelhafez
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rania M M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Huong Le-Petross
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Elsa Arribas
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Deanna L Lane
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David A Spak
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jessica W T Leung
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nabil A Elshafeey
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hagar S Mahmoud
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shu Zhang
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Elizabeth E Ravenberg
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Senthil Damodaran
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Stacy L Moulder
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark D Pagel
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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16
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Feig SA. Prospects for the use of automated whole breast ultrasound: In planning and monitoring breast cancer treatment. Breast J 2021; 27:111-112. [PMID: 33527644 DOI: 10.1111/tbj.14181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Stephen A Feig
- Clinical Radiology, Radiological Sciences School of Medicine, UC Irvine, Irvine, CA, USA
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17
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Potter DA, Herrera-Ponzanelli CA, Hinojosa D, Castillo R, Hernandez-Cruz I, Arrieta VA, Franklin MJ, Yee D. Recent advances in neoadjuvant therapy for breast cancer. Fac Rev 2021; 10:2. [PMID: 33659921 PMCID: PMC7894264 DOI: 10.12703/r/10-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Neoadjuvant trials for early breast cancer have accelerated the identification of novel active agents, enabling streamlined conduct of registration trials with fewer subjects. Measurement of neoadjuvant drug effects has also enabled the identification of patients with high risk of distant recurrence and has justified development of additional adjuvant approaches to improve outcomes. Neoadjuvant evaluation of new drugs was significantly improved by the introduction of pathologic complete response (pCR) rate as a quantitative surrogate endpoint for distant disease-free survival (DDFS) and event free survival (EFS). The neoadjuvant phase 2 platform trial I-SPY 2 simultaneously tests multiple drugs across multiple breast cancer subtypes using Bayesian methods of adaptive randomization for assessment of drug efficacy. In addition to the pCR endpoint, the I-SPY 2 trial has demonstrated that the residual cancer burden (RCB) score measures gradations of tumor response that correlate with DDFS and EFS across treatments and subtypes. For HER2-positive and triple-negative breast cancers that have failed to attain pCR with neoadjuvant chemotherapy (NAC), effective modifications of adjuvant treatment have improved outcomes and changed the standard of care for these subtypes. Neoadjuvant therapy is therefore preferred for stage II and III, as well as some stage I, HER2-positive and triple-negative tumors. Neoadjuvant endocrine therapy (NET) strategies have also emerged from innovative trials for stage II and III estrogen receptor (ER)-positive/HER2-negative tumors, as in the ALTERNATE trial. From neoadjuvant trials, opportunities have emerged to de-escalate therapy on the basis of metrics of response to chemotherapy or hormonal therapy. Neoadjuvant therapy for early breast cancer is therefore emerging as a promising approach to accelerate new drug development, optimize treatment strategies, and (where appropriate) de-escalate neoadjuvant therapy.
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Affiliation(s)
| | - César A Herrera-Ponzanelli
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, México
| | - Diego Hinojosa
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, México
| | - Rafael Castillo
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, México
| | - Irwin Hernandez-Cruz
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, México
| | - Victor A Arrieta
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, México
- Department of Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- PECEM, Facultad de Medicina, Universidad Nacional Autónoma de México, México City, México
| | | | - Douglas Yee
- University of Minnesota, Minneapolis, MN, USA
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18
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Atallah D, Arab W, El Kassis N, Nasser Ayoub E, Chahine G, Salem C, Moubarak M. Breast and tumor volumes on 3D-MRI and their impact on the performance of a breast conservative surgery (BCS). Breast J 2020; 27:252-255. [PMID: 33336469 DOI: 10.1111/tbj.14137] [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: 11/26/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 01/11/2023]
Abstract
Breast conservation rate is being increasingly used nowadays as a marker of breast cancer care among hospitals. Searching for the ideal technique to predict the feasibility of BCS is ongoing. For this matter, the preoperative MRIs of 169 patients operated with radical or conservative surgery were reviewed. We estimated the tumor volume (TV) and breast volume (BV) on enhanced 3D-MRI and compared the tumor-to-breast volume ratio (TV/BV) in both groups. The mean ratio was 9.5% in the mastectomy group and 1.7% in the BCS group. A tumor-to-breast volume ratio less than 4% seemed to favor the adoption of a conservative option. Our data suggest that preoperative 3D-MRI can orient the surgical approach by assessing the TV/BV ratio, increasing lumpectomy rates with clear margins and good cosmetic outcome.
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Affiliation(s)
- David Atallah
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Gynecology and Obstetrics, Hôtel-Dieu de France University Hospital, Beirut, Lebanon
| | - Wissam Arab
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Gynecology and Obstetrics, Hôtel-Dieu de France University Hospital, Beirut, Lebanon
| | - Nadine El Kassis
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Gynecology and Obstetrics, Hôtel-Dieu de France University Hospital, Beirut, Lebanon
| | - Eliane Nasser Ayoub
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Anesthesiology, Hôtel-Dieu de France University Hospital, Beirut, Lebanon
| | - Georges Chahine
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Oncology, Hôtel-Dieu de France University Hospital, Beirut, Lebanon
| | - Christine Salem
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Radiology, Hôtel-Dieu de France University Hospital, Beirut, Lebanon
| | - Malak Moubarak
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Gynecology and Obstetrics, Hôtel-Dieu de France University Hospital, Beirut, Lebanon
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19
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Jones EF, Hathi DK, Freimanis R, Mukhtar RA, Chien AJ, Esserman LJ, van’t Veer LJ, Joe BN, Hylton NM. Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy. Cancers (Basel) 2020; 12:E1511. [PMID: 32527022 PMCID: PMC7352259 DOI: 10.3390/cancers12061511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 12/24/2022] Open
Abstract
In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.
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Affiliation(s)
- Ella F. Jones
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Deep K. Hathi
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Rita Freimanis
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Rita A. Mukhtar
- Department of Surgery, University of California, San Francisco, CA 94115, USA;
| | - A. Jo Chien
- School of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA; (A.J.C.); (L.J.v.V.)
| | - Laura J. Esserman
- Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA;
| | - Laura J. van’t Veer
- School of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA; (A.J.C.); (L.J.v.V.)
| | - Bonnie N. Joe
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Nola M. Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
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20
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Onishi N, Li W, Gibbs J, Wilmes LJ, Nguyen A, Jones EF, Arasu V, Kornak J, Joe BN, Esserman LJ, Newitt DC, Hylton NM. Impact of MRI Protocol Adherence on Prediction of Pathological Complete Response in the I-SPY 2 Neoadjuvant Breast Cancer Trial. Tomography 2020; 6:77-85. [PMID: 32548283 PMCID: PMC7289255 DOI: 10.18383/j.tom.2020.00006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
We investigated the impact of magnetic resonance imaging (MRI) protocol adherence on the ability of functional tumor volume (FTV), a quantitative measure of tumor burden measured from dynamic contrast-enhanced MRI, to predict response to neoadjuvant chemotherapy. We retrospectively reviewed dynamic contrast-enhanced breast MRIs for 990 patients enrolled in the multicenter I-SPY 2 TRIAL. During neoadjuvant chemotherapy, each patient had 4 MRI visits (pretreatment [T0], early-treatment [T1], inter-regimen [T2], and presurgery [T3]). Protocol adherence was rated for 7 image quality factors at T0-T2. Image quality factors confirmed by DICOM header (acquisition duration, early phase timing, field of view, and spatial resolution) were adherent if the scan parameters followed the standardized imaging protocol, and changes from T0 for a single patient's visits were limited to defined ranges. Other image quality factors (contralateral image quality, patient motion, and contrast administration error) were considered adherent if imaging issues were absent or minimal. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of FTV change (percent change of FTV from T0 to T1 and T2) in predicting pathological complete response. FTV changes with adherent image quality in all factors had higher estimated AUC than those with non-adherent image quality, although the differences did not reach statistical significance (T1, 0.71 vs. 0.66; T2, 0.72 vs. 0.68). These data highlight the importance of MRI protocol adherence to predefined scan parameters and the impact of data quality on the predictive performance of FTV in the breast cancer neoadjuvant setting.
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Affiliation(s)
| | - Wen Li
- Departments of Radiology & Biomedical Imaging
| | | | | | - Alex Nguyen
- Departments of Radiology & Biomedical Imaging
| | | | | | | | | | - Laura J Esserman
- Surgery, University of California, San Francisco, San Francisco, CA
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21
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Ali A, Dumbrava M, Riddell K, Stewart N, Ward R, Ibrahim AK, Chin M. Correlation between initial tumour volume and treatment duration on Dabrafenib: observation study of subjects with BRAF mutant melanoma on the BRF112680 trial. BMC Cancer 2020; 20:342. [PMID: 32321474 PMCID: PMC7179008 DOI: 10.1186/s12885-020-06848-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 04/07/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Planar-based measurements of lesions in metastatic melanoma have limitations in estimating tumor burden of a patient and in predicting response to treatment. Volumetric imaging might add predictive value to Response criteria in Solid Tumor (RECIST)-measurement. Based on clinical observations, we explored the association between baseline tumor volume (TV) and duration of treatment with dabrafenib in patients with metastatic melanoma. We have also explored the prognostic value of TV for overall survival (OS) and progression free survival (PFS). METHODS This is a retrospective, chart-review of primary source documents and medical imaging of a cohort of patients participating in the BRF112680 phase 1 clinical trial at the Prince of Wales Hospital. TV was quantified by contouring all the measurable baseline target lesions in the standard manner for radiation planning using Voxxar™ software. We used Cox regression models to analyse associations between TV and duration of treatment with dabrafenib and between TV, PFS and OS. RESULTS Among 13 patients of BRAF 112680 trial, 10 were included in the retrospective analysis. Target lesion sum volume ranged from 0.3 to 1065.5 cm3 (cc), with a median of 27.5 cc. The median PFS and OS were 420 days (range 109-1765) and 1680 days (range 390-2940), respectively. The initial TV was inversely correlated with duration of treatment with dabrafenib (rho - 0.6; P 0.03). In multivariate analysis, TV was a predictor for OS (HR 2.81 CI 1.06-6.19) and PFS (8.76 (CI 1.05-43.58). Patients with tumour volume above the median had significantly lower OS of 6-months compared to 56-months survival for patients with smaller volumes; P = 0.019. CONCLUSIONS TV is a predictor for treatment duration and is prognostic of OS and PFS in patients with metastatic melanoma. These findings need to be validated prospectively in clinical trials.
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Affiliation(s)
- Arwa Ali
- Medical Oncology, Nelune Comprehensive Cancer Centre/The Bright Alliance Building, Prince Of Wales Hospital, Randwick, NSW, 2031, Australia. .,Medical Oncology Department, South Egypt Cancer Institute, Assiut University, Asyut, Egypt.
| | - Monica Dumbrava
- Medical Oncology Department, North West Regional Hospital, Burnie, Tasmania, Australia
| | - Kylie Riddell
- GlaxoSmithKline Research and Development, Ermington, Australia
| | - Nina Stewart
- Radiation Oncology Department, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Robyn Ward
- Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
| | - Ahmed K Ibrahim
- Community Health School, Faculty of Medicine, Assiut University, Asyut, Egypt
| | - Melvin Chin
- Medical Oncology, Nelune Comprehensive Cancer Centre/The Bright Alliance Building, Prince Of Wales Hospital, Randwick, NSW, 2031, Australia
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22
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Zhou J, Lu J, Gao C, Zeng J, Zhou C, Lai X, Cai W, Xu M. Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI. BMC Cancer 2020; 20:100. [PMID: 32024483 PMCID: PMC7003343 DOI: 10.1186/s12885-020-6523-2] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/07/2020] [Indexed: 02/06/2023] Open
Abstract
Background The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). Methods Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). Results The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. Conclusions Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.
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Affiliation(s)
- Jiali Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Ningbo First Hospital, Ningbo, China
| | - Jinghui Lu
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., 400C, Boston, MA, 02114, USA
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingjing Zeng
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Changyu Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaobo Lai
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Wenli Cai
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., 400C, Boston, MA, 02114, USA.
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China. .,The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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23
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Fan M, Xia P, Liu B, Zhang L, Wang Y, Gao X, Li L. Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Breast Cancer Res 2019; 21:112. [PMID: 31623683 PMCID: PMC6798414 DOI: 10.1186/s13058-019-1199-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 09/13/2019] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Heterogeneity is a common finding within tumours. We evaluated the imaging features of tumours based on the decomposition of tumoural dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to identify their prognostic value for breast cancer survival and to explore their biological importance. METHODS Imaging features (n = 14), such as texture, histogram distribution and morphological features, were extracted to determine their associations with recurrence-free survival (RFS) in patients in the training cohort (n = 61) from The Cancer Imaging Archive (TCIA). The prognostic value of the features was evaluated in an independent dataset of 173 patients (i.e. the reproducibility cohort) from the TCIA I-SPY 1 TRIAL dataset. Radiogenomic analysis was performed in an additional cohort, the radiogenomic cohort (n = 87), using DCE-MRI from TCGA-BRCA and corresponding gene expression data from The Cancer Genome Atlas (TCGA). The MRI tumour area was decomposed by convex analysis of mixtures (CAM), resulting in 3 components that represent plasma input, fast-flow kinetics and slow-flow kinetics. The prognostic MRI features were associated with the gene expression module in which the pathway was analysed. Furthermore, a multigene signature for each prognostic imaging feature was built, and the prognostic value for RFS and overall survival (OS) was confirmed in an additional cohort from TCGA. RESULTS Three image features (i.e. the maximum probability from the precontrast MR series, the median value from the second postcontrast series and the overall tumour volume) were independently correlated with RFS (p values of 0.0018, 0.0036 and 0.0032, respectively). The maximum probability feature from the fast-flow kinetics subregion was also significantly associated with RFS and OS in the reproducibility cohort. Additionally, this feature had a high correlation with the gene expression module (r = 0.59), and the pathway analysis showed that Ras signalling, a breast cancer-related pathway, was significantly enriched (corrected p value = 0.0044). Gene signatures (n = 43) associated with the maximum probability feature were assessed for associations with RFS (p = 0.035) and OS (p = 0.027) in an independent dataset containing 1010 gene expression samples. Among the 43 gene signatures, Ras signalling was also significantly enriched. CONCLUSIONS Dynamic pattern deconvolution revealed that tumour heterogeneity was associated with poor survival and cancer-related pathways in breast cancer.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Pingping Xia
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Bin Liu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Lin Zhang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China.
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Waninger JJ, Green MD, Cheze Le Rest C, Rosen B, El Naqa I. Integrating radiomics into clinical trial design. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:339-346. [PMID: 31527581 DOI: 10.23736/s1824-4785.19.03217-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In radiomics, quantitative features that describe phenotypic tumor characteristics are derived from radiographic images. Because radiomics generates information from routine medical images, it is a powerful way to non-invasively examine the spatial and temporal heterogeneity of disease, and thus has potential to significantly impact clinical trial design, execution, and ultimately patient care. The aim of this review article is to discuss how radiomics may address some of the current challenges in clinical randomized control trials, and the difficulties of integrating robust and repeatable radiomics analysis into trial design. Each step of the radiomics process, including image acquisition and reconstruction, image segmentation, feature extraction, and computational analysis, requires extensive standardization in order to be successfully incorporated into clinical trials and inform clinical decision making. By addressing these challenges, the potential of radiomics may be realized.
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Affiliation(s)
- Jessica J Waninger
- Department of Medical Education, University of Michigan School of Medicine, Ann Arbor, MI, USA.,Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael D Green
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA.,University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | | | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA -
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25
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Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019; 104:302-315. [PMID: 30711529 PMCID: PMC6499656 DOI: 10.1016/j.ijrobp.2019.01.087] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 01/17/2019] [Accepted: 01/23/2019] [Indexed: 02/06/2023]
Abstract
Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.
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Affiliation(s)
- Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey.
| | - Hania Al-Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mi Huang
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael V Knopp
- Division of Imaging Science, Department of Radiology, Ohio State University, Columbus, Ohio
| | - Jeff M Michalski
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - James Monroe
- Department of Radiation Oncology, St. Anthony's Cancer Center, St. Louis, Missouri
| | - Ceferino Obcemea
- Radiation Research Program, National Cancer Institute, Bethesda, Maryland
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Timothy Solberg
- Department of Radiation Oncology, University of California-San Francisco, San Francisco, California
| | - Jackie Wu
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Ping Xia
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Ying Xiao
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Issam El Naqa
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
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26
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Li W, Newitt DC, Wilmes LJ, Jones EF, Arasu V, Gibbs J, La Yun B, Li E, Partridge SC, Kornak J, Esserman LJ, Hylton NM. Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL. J Magn Reson Imaging 2019; 50:1742-1753. [PMID: 31026118 DOI: 10.1002/jmri.26770] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 04/18/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The change in apparent diffusion coefficient (ADC) measured from diffusion-weighted imaging (DWI) has been shown to be predictive of pathologic complete response (pCR) for patients with locally invasive breast cancer undergoing neoadjuvant chemotherapy. PURPOSE To investigate the additive value of tumor ADC in a multicenter clinical trial setting. STUDY TYPE Retrospective analysis of multicenter prospective data. POPULATION In all, 415 patients who enrolled in the I-SPY 2 TRIAL from 2010 to 2014 were included. FIELD STRENGTH/SEQUENCE 1.5T or 3T MRI system using a fat-suppressed single-shot echo planar imaging sequence with b-values of 0 and 800 s/mm2 for DWI, followed by a T1-weighted sequence for dynamic contrast-enhanced MRI (DCE-MRI) performed at pre-NAC (T0), after 3 weeks of NAC (T1), mid-NAC (T2), and post-NAC (T3). ASSESSMENT Functional tumor volume and tumor ADC were measured at each MRI exam; pCR measured at surgery was assessed as the binary outcome. Breast cancer subtype was defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. STATISTICAL TESTS A logistic regression model was used to evaluate associations between MRI predictors with pCR. The cross-validated area under the curve (AUC) was calculated to assess the predictive performance of the model with and without ADC. RESULTS In all, 354 patients (128 HR+/HER2-, 60 HR+/HER2+, 34 HR-/HER2+, 132 HR-/HER2-) were included in the analysis. In the full cohort, adding ADC predictors increased the AUC from 0.76 to 0.78 at mid-NAC and from 0.76 to 0.81 at post-NAC. In HR/HER2 subtypes, the AUC increased from 0.52 to 0.65 at pre-NAC for HR+/HER2-, from 0.67 to 0.73 at mid-NAC and from 0.72 to 0.76 at post-NAC for HR+/HER2+, from 0.71 to 0.81 at post-NAC for triple negatives. DATA CONCLUSION The addition of ADC to standard functional tumor volume MRI showed improvement in the prediction of treatment response in HR+ and triple-negative breast cancer. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2019;50:1742-1753.
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Affiliation(s)
- Wen Li
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - David C Newitt
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Lisa J Wilmes
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Ella F Jones
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Vignesh Arasu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Jessica Gibbs
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Bo La Yun
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA.,Department of Radiology, Seoul National University Bundang Hospital, Seoul, Korea
| | - Elizabeth Li
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA.,Department of Biomedical Engineering, University of California, Davis, California, USA
| | | | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
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- Quantum Leap Healthcare Collaborative, San Francisco, California, USA
| | - Laura J Esserman
- Department of Surgery, University of California, San Francisco, California, USA
| | - Nola M Hylton
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
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Jones EF, Ray KM, Li W, Chien AJ, Mukhtar RA, Esserman LJ, Franc BL, Seo Y, Pampaloni MH, Joe BN, Hylton NM. Initial experience of dedicated breast PET imaging of ER+ breast cancers using [F-18]fluoroestradiol. NPJ Breast Cancer 2019; 5:12. [PMID: 31016232 PMCID: PMC6467896 DOI: 10.1038/s41523-019-0107-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/05/2019] [Indexed: 12/15/2022] Open
Abstract
Dedicated breast positron emission tomography (dbPET) is an emerging technology with high sensitivity and spatial resolution that enables detection of sub-centimeter lesions and depiction of intratumoral heterogeneity. In this study, we report our initial experience with dbPET using [F-18]fluoroestradiol (FES) in assessing ER+ primary breast cancers. Six patients with >90% ER+ and HER2- breast cancers were imaged with dbPET and breast MRI. Two patients had ILC, three had IDC, and one had an unknown primary tumor. One ILC patient was treated with letrozole, and another patient with IDC was treated with neoadjuvant chemotherapy without endocrine treatment. In this small cohort, we observed FES uptake in ER+ primary breast tumors with specificity to ER demonstrated in a case with tamoxifen blockade. FES uptake in ILC had a diffused pattern compared to the distinct circumscribed pattern in IDC. In evaluating treatment response, the reduction of SUVmax was observed with residual disease in an ILC patient treated with letrozole, and an IDC patient treated with chemotherapy. Future study is critical to understand the change in FES SUVmax after endocrine therapy and to consider other tracer uptake metrics with SUVmax to describe ER-rich breast cancer. Limitations include variations of FES uptake in different ER+ breast cancer diseases and exclusion of posterior tissues and axillary regions. However, FES-dbPET has a high potential for clinical utility, especially in measuring response to neoadjuvant endocrine treatment. Further development to improve the field of view and studies with a larger cohort of ER+ breast cancer patients are warranted.
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Affiliation(s)
- Ella F. Jones
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Kimberly M. Ray
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Wen Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Amy J. Chien
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA USA
| | - Rita A. Mukhtar
- Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA USA
| | - Laura J. Esserman
- Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA USA
| | - Benjamin L. Franc
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Youngho Seo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Miguel H. Pampaloni
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Bonnie N. Joe
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Nola M. Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
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28
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Sarıtaş AG, Yalav O, Kekeç Y, Sakman G, Kılıç E, Zorludemir S, Ergin M, Eray İC, Kara O, Paydaş S. Lokal ileri evre meme kanserli hastalarda neoadjuvan kemoterapinin ER, PR, c-erb2, VEGF ve Ki-67 üzerine etkisi. CUKUROVA MEDICAL JOURNAL 2019. [DOI: 10.17826/cumj.412192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Böhm I, Gehrke S, Kleb B, Hungerbühler M, Müller R, Klose KJ, Alfke H. Monitoring of tumor burden in vivo by optical imaging in a xenograft SCID mouse model: evaluation of two fluorescent proteins of the GFP-superfamily. Acta Radiol 2019; 60:315-326. [PMID: 29890843 DOI: 10.1177/0284185118780896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Mouse models of human-malignant-melanoma (MM) are important tools to study tumor dynamics. The enhanced green fluorescent protein (EGFP) is widely used in molecular imaging approaches, together with optical scanners, and fluorescence imaging. PURPOSE Currently, there are no data available as to whether other fluorescent proteins are more suitable. The goal of this preclinical study was to analyze two fluorescent proteins of the GFP superfamily under real-time in vivo conditions using fluorescence reflectance imaging (FRI). MATERIAL AND METHODS The human melanoma cell line MeWo was stable transfected with one plasmid: pEGFP-C1 or pDsRed1-N1. We investigated two severe combined immunodeficiency (SCID)-mice groups: A (solid xenografts) and B (xenografts as metastases). After three weeks, the animals were weekly imaged by FRI. Afterwards the mice were euthanized and metastases were imaged in situ: to quantify the cutis-dependent reduction of emitted light, we compared signal intensities obtained by metastases in vivo with signal intensities obtained by in situ liver parenchyma preparations. RESULTS More than 90% of cells were stable transfected. EGFP-/DsRed-xenograft tumors had identical growth kinetics. In vivo the emitted light by DsRed tumors/metastases was much brighter than by EGFP. DsRed metastases were earlier (3 vs. 5 weeks) and much more sensitive detectable than EGFP metastases. Cutis-dependent reduction of emitted light was greater in EGFP than in DsRed mice (tenfold). Autofluorescence of DsRed was lower than of EGFP. CONCLUSION We established an in vivo xenograft mouse model (DsRed-MeWo) that is reliable, reproducible, and superior to the EGFP model as a preclinical tool to study innovative therapies by FRI under real-time in vivo conditions.
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Affiliation(s)
- Ingrid Böhm
- Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland
- Radiology Laboratory, Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Stephan Gehrke
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Beate Kleb
- Department of Experimental Ophthalmology, Philipps University of Marburg, Marburg, Germany
| | - Martin Hungerbühler
- Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland
- Radiology Laboratory, Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Rolf Müller
- Institute of Molecular Tumor Biology and Cancer Gene Therapy (IMT), Philipps University of Marburg, Marburg, Germany
| | - Klaus J Klose
- Deans Office, Faculty of Medicine, Philipps University of Marburg, Marburg, Germany
| | - Heiko Alfke
- Department of Diagnostic Radiology and Interventional Radiology, Klinikum Lüdenscheid, Lüdenscheid, Germany
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30
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Mazurowski MA, Saha A, Harowicz MR, Cain EH, Marks JR, Marcom PK. Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer. J Magn Reson Imaging 2019; 49:e231-e240. [PMID: 30672045 DOI: 10.1002/jmri.26648] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/23/2018] [Accepted: 12/26/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND While important in diagnosis of breast cancer, the scientific assessment of the role of imaging in prognosis of outcomes and treatment planning is limited. PURPOSE To evaluate the potential of using quantitative imaging variables for stratifying risk of distant recurrence in breast cancer patients. STUDY TYPE Retrospective. POPULATION In all, 892 female invasive breast cancer patients. SEQUENCE Dynamic contrast-enhanced MRI with field strength 1.5 T and 3 T. ASSESSMENT Computer vision algorithms were applied to extract a comprehensive set of 529 imaging features quantifying size, shape, enhancement patterns, and heterogeneity of the tumors and the surrounding tissue. Using a development set with 446 cases, we selected 20 imaging features with high prognostic value. STATISTICAL TESTS We evaluated the imaging features using an independent test set with 446 cases. The principal statistical measure was a concordance index between individual imaging features and patient distant recurrence-free survival (DRFS). RESULTS The strongest association with DRFS that persisted after controlling for known prognostic clinical and pathology variables was found for signal enhancement ratio (SER) partial tumor volume (concordance index [C] = 0.768, 95% confidence interval [CI]: 0.679-0.856), tumor major axis length (C = 0.742, 95% CI: 0.650-0.834), kurtosis of the SER map within tumor (C = 0.640, 95% CI: 0.521-0.760), tumor cluster shade (C = 0.313, 95% CI: 0.216-0.410), and washin rate information measure of correlation (C = 0.702, 95% CI: 0.601-0.803). DATA CONCLUSION Quantitative assessment of breast cancer features seen in a routine breast MRI might be able to be used for assessment of risk of distant recurrence. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.
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Affiliation(s)
- Maciej A Mazurowski
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Ashirbani Saha
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Michael R Harowicz
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Elizabeth Hope Cain
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jeffrey R Marks
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Surgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - P Kelly Marcom
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
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31
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Jia K, Li L, Wu XJ, Hao MJ, Xue HY. Contrast-enhanced ultrasound for evaluating the pathologic response of breast cancer to neoadjuvant chemotherapy: A meta-analysis. Medicine (Baltimore) 2019; 98:e14258. [PMID: 30681622 PMCID: PMC6358361 DOI: 10.1097/md.0000000000014258] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE Recent reports have suggested that contrast-enhanced ultrasound (CEUS) can be used to monitor the pathologic responses of breast cancer (BC) to neoadjuvant chemotherapy (NAC); however, the diagnostic performance of CEUS in BC has yet to be confirmed. Thus, we conducted a meta-analysis of related studies to explore the relationship between CEUS and pathologic responses of BC to NAC. MATERIALS AND METHODS We searched PubMed, Embase, Web of Science, ScienceDirect, and China National Knowledge Infrastructure databases for studies published until September 31, 2018. Study-specific odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and then ORs with 95% CIs were pooled to estimate the prognostic role of CEUS for the pathologic responses of BC to NAC. RESULTS Pooled meta-analysis of the 9 eligible studies that included 424 patients indicated the high performance of CEUS for monitoring pathologic responses to NAC (OR = 31.83, 95% CI: 16.69-60.67, P < .001), with no significant heterogeneity (I = 0.0%, P = .529). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 87% (95% CI: 0.81-0.92), 84% (95% CI: 0.74-0.91), 5.5 (95% CI: 3.3-9.2), 0.15 (95% CI: 0.10-0.23), and 36 (95% CI: 18-70), respectively. An area under the curve of 0.92 (95% CI: 0.89-0.94) suggests a high ability for prognostic detection. Although Begg's funnel plot (P = .057) indicated the presence of publication bias among the included studies, the trim-and-fill method verified the stability of the pooled outcomes. Sensitivity analysis suggested that the pooled OR was robust. CONCLUSION Our results suggest that CEUS has a high diagnostic performance for the pathologic responses of BC to NAC. Further and better-designed studies should be performed to verify the clinical applications of CEUS for monitoring BC responses to NAC.
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Hwang KT, Han W, Lee SM, Choi J, Kim J, Rhu J, Kim YA, Noh DY. Prognostic influence of 3-dimensional tumor volume on breast cancer compared to conventional 1-dimensional tumor size. Ann Surg Treat Res 2018; 95:183-191. [PMID: 30310801 PMCID: PMC6172357 DOI: 10.4174/astr.2018.95.4.183] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 04/22/2018] [Accepted: 06/01/2018] [Indexed: 12/30/2022] Open
Abstract
Purpose The prognostic influence of 3-dimensional tumor volume (Tv) on breast cancer compared to conventional 1-dimensional tumor size (T) was investigated. Methods Analysis was performed on a cohort of 8,996 primary breast cancer patients who were initially diagnosed with TNM stage I–III. Tumor size was defined as the maximum tumor dimension, and Tv was calculated by the equation of (4π × r1 × r2 × r3)/3; r1, r2, and r3 were defined as half of the largest, intermediate, and shortest dimension of the tumor, respectively. Tv was classified into Tv1, Tv2, and Tv3 according to the cut off values of 2.056 cm3 and 20.733 cm3. Results The survival curves according to both the T and Tv categories were clearly differentiated (all P < 0.001), as were those for staging by T and Tv (all P < 0.001). In T1 and T2 tumors, the Tv1 group showed superior survival over the Tv2 group (T1, P < 0.001; T2, P = 0.001). Univariate and multivariate analysis both indicated that Tv was a significant prognostic factor (both P < 0.001). The receiver operating characteristic curve showed that the area under the curves were 0.712 (P < 0.001) for Tv and 0.699 (P < 0.001) for T. Positive correlations were observed between the number of positive nodes and T (coefficient = 0.325; P < 0.001), and between the number of positive nodes and Tv (coefficient = 0.321; P < 0.001). Conclusion Tv classification works well for predicting the prognosis of breast cancer, and it is a better predictor than conventional T classification in several aspects. Further studies are needed to validate the practical usefulness of Tv classification in clinical settings.
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Affiliation(s)
- Ki-Tae Hwang
- Department of Surgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, Korea
| | - Wonshik Han
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | | | | | - Jongjin Kim
- Department of Surgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, Korea
| | - Jiyoung Rhu
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Young A Kim
- Department of Pathology, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, Korea
| | - Dong-Young Noh
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
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33
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Huang SY, Franc BL, Harnish RJ, Liu G, Mitra D, Copeland TP, Arasu VA, Kornak J, Jones EF, Behr SC, Hylton NM, Price ER, Esserman L, Seo Y. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ Breast Cancer 2018; 4:24. [PMID: 30131973 PMCID: PMC6095872 DOI: 10.1038/s41523-018-0078-2] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 07/10/2018] [Accepted: 07/18/2018] [Indexed: 12/20/2022] Open
Abstract
Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10−6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival. Automated analyses of breast scans taken with two types of medical imaging technologies can help oncologists decode clinically relevant features, a finding that could help personalize cancer diagnosis and treatment. Youngho Seo from the University of California, San Francisco, USA, and coworkers extracted 84 quantitative features from positron emission tomography and magnetic resonance imaging scans performed on 113 women with breast cancer. The researchers then applied data-characterization and pattern-recognition algorithms—which included machine-learning methods and engineered features coded by experts—to create classification models that helped uncover disease characteristics that were not obvious to the naked eye. These models successfully subdivided patients according to tumor grade, overall stage, cancer subtype and disease recurrence risk, providing proof of principle that radiomic analyses of this kind could provide valuable information for personalized management of breast cancer.
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Affiliation(s)
- Shih-Ying Huang
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Benjamin L Franc
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Roy J Harnish
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Gengbo Liu
- 2School of Computing, Florida Institute of Technology, Melbourne, FL USA
| | - Debasis Mitra
- 2School of Computing, Florida Institute of Technology, Melbourne, FL USA
| | - Timothy P Copeland
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Vignesh A Arasu
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - John Kornak
- 3Department of Epidemiology and Biostatistics, University of California, San Francisco, CA USA
| | - Ella F Jones
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Spencer C Behr
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Nola M Hylton
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Elissa R Price
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Laura Esserman
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA.,4Department of Surgery, University of California, San Francisco, CA USA
| | - Youngho Seo
- 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA.,5Department of Radiation Oncology, University of California, San Francisco, CA USA.,6Joint Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, Berkeley, CA USA
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Henderson SA, Muhammad Gowdh N, Purdie CA, Jordan LB, Evans A, Brunton T, Thompson AM, Vinnicombe S. Breast cancer: influence of tumour volume estimation method at MRI on prediction of pathological response to neoadjuvant chemotherapy. Br J Radiol 2018; 91:20180123. [PMID: 29641224 PMCID: PMC6221785 DOI: 10.1259/bjr.20180123] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/20/2018] [Accepted: 04/06/2018] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE Does method of tumour volume measurement on MRI influence prediction of treatment outcome in patients with primary breast cancer undergoing neoadjuvant chemotherapy (NAC)?. METHOD The study comprised of 136 women with biopsy-proven breast cancer scheduled for MRI monitoring during NAC treatment. Dynamic contrast-enhanced images were acquired at baseline (pre-NAC) and interim (post three NAC cycles) time points. Functional tumour volumes (FTVs), automatically derived using vendor software and enhancing tumour volumes (ETVs), user-derived using a semi-automated thresholding technique, were calculated at each time point and percentage changes calculated. Response, assessed using residual cancer burden (RCB) score on surgically resected specimens, was compared statistically with volumetric changes and receiver operating characteristic analysis performed. RESULTS Mean volumetric differences for each RCB response category were (FTV/ETV): pathological complete response (pCR) 95.5/96.8%, RCB-I 69.8/66.7%, RCB-II 64.0/65.5%, RCB-III 25.4/24.0%. Differences were significant between pCR and RCB-II/RCB-III categories (p < 0.040; unpaired t-test) using FTV measures and between pCR and RCB-I/RCB-II/RCB-III categories (p < 0.006; unpaired t-test) when ETV was used. Receiver operating characteristic analysis for pCR identification post-NAC yielded area under the curve for FTV/ETV of 0.834/0.920 respectively. Sensitivity and specificity for FTV was 80.0 and 76.8% for FTV and 81.0 and 91.8% for ETV. CONCLUSION ETV changes can identify patients likely to achieve a complete response to NAC. Potentially, this could impact patient management regarding the possible avoidance of post-NAC surgery. Advances in Knowledge: Interim changes in ETV are more useful than FTV in predicting final pathological response to NAC. ETV differentiates patients who will achieve a complete response from those who will have residual disease.
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Affiliation(s)
| | | | | | - Lee B Jordan
- Department of Pathology, Ninewells Hospital, Dundee, UK
| | - Andrew Evans
- Division of Imaging and Technology, University of Dundee, Dundee, UK
| | - Tracy Brunton
- MRI Department, Clinical Research Centre, University of Dundee, Dundee, UK
| | - Alastair M Thompson
- Department of Breast Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sarah Vinnicombe
- Division of Imaging and Technology, University of Dundee, Dundee, UK
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van Egdom LSE, Lagendijk M, Heijkoop EHM, Koning AHJ, van Deurzen CHM, Jager A, van Lankeren W, Koppert LB. Three-dimensional ultrasonography of the breast; An adequate replacement for MRI in neoadjuvant chemotherapy tumour response evaluation? - RESPONDER trial. Eur J Radiol 2018; 104:94-100. [PMID: 29857873 DOI: 10.1016/j.ejrad.2018.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 04/16/2018] [Accepted: 05/04/2018] [Indexed: 12/30/2022]
Abstract
BACKGROUND Accurate measurement of tumour response during and after neoadjuvant chemotherapy (NAC) is important and may influence treatment decisions in invasive breast cancer patients. Breast MRI forms the gold standard but is more burdensome, time consuming and costly. In this study response measurement was done with 3-D ultrasound by Automated Breast Volume Scanner (ABVS) and compared to breast MRI. Moreover, patient satisfaction with both techniques was compared. METHODS AND MATERIALS A single-institution, prospective observational pilot study evaluating tumour response by ABVS in addition to breast MRI (standard care) was performed in 25 invasive breast cancer patients receiving NAC. Tumour response was evaluated comparing longest tumour diameters as well as tumour volumes at predefined time points using Bland-Altman analysis. Volume measurements for breast MRI were obtained using a fully immersive virtual reality system (a Barco I-Space) and V-Scope software. Same software was used to obtain ABVS volume measurements using an in-house developed desktop VR system. Inter- and intra-observer agreement was evaluated by Intraclass Correlation Coefficient (ICC). RESULTS Twenty-five patients were eligible for baseline measurement, 20 for a mid-NAC response evaluation, and five for a post-NAC response evaluation. MRI and ABVS showed absolute concordance in 73% of patients for the mid-NAC evaluation, with a 'good' correlation for the difference in longest diameter measurement (ICC 0.73, p < 0.01) as compared to baseline assessment. Concerning difference in volume measurement in the mid-NAC response evaluation showed a 'fair' correlation (ICC 0.52, p < 0.01) and in the post-NAC response evaluation an 'excellent' correlation (ICC 0.98, p < 0.01). 'Excellent' inter- and intra-observer agreement was found (ICC 0.88, p < 0.01) with comparable limits of agreement (LOA) for observer 1 and 2 in both diameter and volume measurement. Patient satisfaction was higher for ABVS compared to breast MRI, 93% versus 12% respectively. CONCLUSION ABVS showed 'good' correlation with MRI tumour response evaluation in breast cancer patients during NAC with 'excellent' inter- and intra-observer agreement. ABVS has patients' preference over breast MRI and could be considered as alternative to breast MRI, in case results on an on-going prospective trial confirm these results (NTR6799).
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Affiliation(s)
- L S E van Egdom
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands.
| | - M Lagendijk
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands
| | - E H M Heijkoop
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands
| | - A H J Koning
- Department of Bio-informatics, Erasmus MC, 's-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands
| | - C H M van Deurzen
- Department of Pathology, Erasmus MC, 's Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands
| | - A Jager
- Department of Medical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands
| | - W van Lankeren
- Department of Radiology, Erasmus MC, 's-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands
| | - L B Koppert
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands
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DCE-MRI and parametric imaging in monitoring response to neoadjuvant chemotherapy in breast carcinoma: a preliminary report. Pol J Radiol 2018; 83:e220-e228. [PMID: 30627239 PMCID: PMC6323583 DOI: 10.5114/pjr.2018.76271] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 06/29/2017] [Indexed: 12/30/2022] Open
Abstract
Purpose Neoadjuvant chemotherapy is recommended in patients with locally advanced breast cancer. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables evaluation of the tumour neovasculature that occurs prior to any volume change, which helps identify early treatment failures and allows prompt implementation of second-line therapy. Material and methods We conducted a prospective study in 14 patients with histopathologically proven breast cancer. DCE-MRI data were acquired using multisection, T1-weighted, 3D vibe sequences with fat suppression before, during, and after IV bolus injection (0.1 mmol/kg body weight, Gadoversetamide, Optimark). Post-processing of dynamic contrast perfusion data was done with the vendor’s Tissue 4D software to generate various dynamic contrast parameters, i.e. Ktrans, Kep, Ve, initial area under the time signal curve (IAUC), apparent diffusion coefficient (ADC), and enhancement curve. Patients underwent MRI examinations at baseline, and then after two cycles, and finally at completion of chemotherapy. Results Based on Sataloff criteria for pathological responses, four patients out of 14 were responders, and 10 were non-responders. At the 2nd MRI examination, IAUC was significantly smaller in responders than in non-responders (p = 0.023). When the results of the first and second MRI examinations were compared, Kep decreased from baseline to the second MRI (p = 0.03) in non-responders and in responders (p = 0.04). This change was statistically significant in both groups. The ADC values increased significantly in responders from baseline to the third MRI (p = 0.012). Conclusions In our study, IAUC and ADC were the only parameters that reliably differentiated responders from non-responders after two and three cycles of chemotherapy.
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Saha A, Harowicz MR, Mazurowski MA. Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors. Med Phys 2018; 45:3076-3085. [PMID: 29663411 DOI: 10.1002/mp.12925] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 03/01/2018] [Accepted: 04/04/2018] [Indexed: 12/30/2022] Open
Abstract
PURPOSE To review features used in MRI radiomics of breast cancer and study the inter-reader stability of the features. METHODS We implemented 529 algorithmic features that can be extracted from tumor and fibroglandular tissue (FGT) in breast MRIs. The features were identified based on a review of the existing literature with consideration of their usage, prognostic ability, and uniqueness. The set was then extended so that it comprehensively describes breast cancer imaging characteristics. The features were classified into 10 groups based on the type of data used to extract them and the type of calculation being performed. For the assessment of inter-reader variability, four fellowship-trained readers annotated tumors on preoperative dynamic contrast-enhanced MRIs for 50 breast cancer patients. Based on the annotations, an algorithm automatically segmented the image and extracted all features resulting in one set of features for each reader. For a given feature, the inter-reader stability was defined as the intraclass correlation coefficient (ICC) computed using the feature values obtained through all readers for all cases. RESULTS The average inter-reader stability for all features was 0.8474 (95% CI: 0.8068-0.8858). The mean inter-reader stability was lower for tumor-based features (0.6348, 95% CI: 0.5391-0.7257) than FGT-based features (0.9984, 95% CI: 0.9970-0.9992). The feature group with the highest inter-reader stability quantifies breast and FGT volume. The feature group with the lowest inter-reader stability quantifies variations in tumor enhancement. CONCLUSIONS Breast MRI radiomics features widely vary in terms of their stability in the presence of inter-reader variability. Appropriate measures need to be taken for reducing this variability in tumor-based radiomics.
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Affiliation(s)
- Ashirbani Saha
- Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Michael R Harowicz
- Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Maciej A Mazurowski
- Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.,Department of Electrical and Computer Engineering, Duke University, Box 90291, Durham, NC, 27708, USA.,Duke University Medical Physics Program, DUMC 2729, 2424 Erwin Road, Suite 101, Durham, NC, 27705, USA
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Raghavendra AS, Tripathy D. How Does MR Imaging Help Care for the Breast Cancer Patient? Perspective of a Medical Oncologist. Magn Reson Imaging Clin N Am 2018; 26:289-293. [DOI: 10.1016/j.mric.2017.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Dogan BE, Yuan Q, Bassett R, Guvenc I, Jackson EF, Cristofanilli M, Whitman GJ. Comparing the Performances of Magnetic Resonance Imaging Size vs Pharmacokinetic Parameters to Predict Response to Neoadjuvant Chemotherapy and Survival in Patients With Breast Cancer. Curr Probl Diagn Radiol 2018; 48:235-240. [PMID: 29685400 DOI: 10.1067/j.cpradiol.2018.03.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 03/16/2018] [Indexed: 11/22/2022]
Abstract
PURPOSE To compare the value of dynamic contrast-enhanced magnetic resonance imaging-pharmacokinetic (PK) parameters vs tumor volume in predicting breast cancer neoadjuvant chemotherapy response (NACR) and patient survival. SUBJECTS AND METHODS Sixty-six patients with locally advanced breast cancer who underwent breast MRI monitoring of NACR were retrospectively analyzed. We compared baseline transfer constant (Ktrans), reflux rate contrast (kep), and extracellular extravascular volume fraction (ve) with the same parameters obtained at early postchemotherapy MRI, and examined model-independent changes in time-intensity curves (maximum slope, contrast enhancement ratio, and IAUC90). Tumor size changes (tumor volume, single dimension, and Response Evaluation Criteria in Solid Tumors [RECIST]) were also analyzed. The Spearman correlation test was used to assess the association between size and PK parameters, and regression analysis to assess the association with 5-year disease-free survival. RESULTS Higher ve values at baseline were associated with greater decreases in tumor size (P = 0.008). Changes in Ktrans and IAUC90 were the strongest predictors of NACR. Changes in IAUC90 (P = 0.04) and RECIST (P = 0.003) were independently associated with pathologic response. The only parameter significantly associated with 5-year survival was change in RECIST (P = 0.001). However, there was a trend toward statistical significance for changes in ve and Ktrans, with greater changes associated with longer survival. CONCLUSION Changes in PK and dynamic contrast-enhanced magnetic resonance imaging kinetic parameters may have a role in predicting NACR in breast tumors. Although changes in Ktrans and IAUC90 are helpful in predicting NACR, they do not show significant association with survival. Early RECIST size change measured by MRI remains the strongest predictor of overall patient survival.
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Affiliation(s)
- Basak E Dogan
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX; Department of Diagnostic Radiology, The University of Texas Southwestern Medical Center, Dallas, TX.
| | - Qing Yuan
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Roland Bassett
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Inanc Guvenc
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Edward F Jackson
- Department of Medical Physics, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Massimo Cristofanilli
- Department of Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Gary J Whitman
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX
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Abstract
Neoadjuvant chemotherapy (NAC) has become an important treatment approach for stage II/III breast cancers to downsize tumor and enable breast-conserving surgery for patients that may otherwise undergo mastectomy. MR imaging has the potential to identify early response or disease progression, enabling potential modification to NAC regimens. Detection of size and morphologic changes is better appreciated with MR imaging than other modalities and is different between molecular subtypes of breast cancer. The combination of DCE-MR imaging and DWI provides the highest sensitivity and specificity. Other new modalities such as FDG PET/MR imaging and molecular breast imaging are still undergoing research.
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Affiliation(s)
- Huong T Le-Petross
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, 1155 Pressler Street, Houston, TX 77030, USA.
| | - Bora Lim
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, 1155 Pressler Street, Houston, TX 77030, USA
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Vedantham S, Karellas A. Emerging Breast Imaging Technologies on the Horizon. Semin Ultrasound CT MR 2018; 39:114-121. [PMID: 29317033 DOI: 10.1053/j.sult.2017.09.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Early detection of breast cancers by mammography in conjunction with adjuvant therapy has contributed to reduction in breast cancer mortality. Mammography remains the "gold-standard" for breast cancer screening but is limited by tissue superposition. Digital breast tomosynthesis and more recently, dedicated breast computed tomography have been developed to alleviate the tissue superposition problem. However, all of these modalities rely upon x-ray attenuation contrast to provide anatomical images, and there are ongoing efforts to develop and clinically translate alternative modalities. These emerging modalities could provide for new contrast mechanisms and may potentially improve lesion detection and diagnosis. In this article, several of these emerging modalities are discussed with a focus on technologies that have advanced to the stage of in vivo clinical evaluation.
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Affiliation(s)
- Srinivasan Vedantham
- Department of Medical Imaging, University of Arizona College of Medicine, Banner University Medical Center, Tucson, AZ.
| | - Andrew Karellas
- Department of Medical Imaging, University of Arizona College of Medicine, Banner University Medical Center, Tucson, AZ
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Choi EK, Yoo IR, Kim SH, Park SY, O JH, Kang BJ. The value of pre- and post-neoadjuvant chemotherapy F-18 FDG PET/CT scans in breast cancer: comparison with MRI. Acta Radiol 2018; 59:41-49. [PMID: 28427271 DOI: 10.1177/0284185117705011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Background Accurate assessment of neoadjuvant chemotherapy (NAC) response with positron emission tomography/computed tomography (PET/CT) or magnetic resonance imaging (MRI) may provide appropriate operation guidelines for individual breast cancer patients. Purpose To compare the values of PET/CT and MRI for response evaluation following NAC in breast cancer patients. Material and Methods Thirty-three consecutive patients who underwent NAC were included. PET/CT and MRI were performed before and one to four weeks after NAC. With response evaluation of PET/CT and MRI, patients with complete/partial responses on imaging studies were considered to be responders, and those showing stable/progressive disease non-responders. Peak standardized uptake value corrected for lean body mass (SULpeak) and metabolic tumor volume (MTV) were measured from PET/CT, and unidimensional diameter (1D) and tumor volume (TV) from MRI. Reduction rates for each parameter were calculated (Δ%SULpeak, Δ%MTV, Δ%1D, and Δ%TV). The pathological response for NAC as reference was evaluated after surgical resection of the remaining tumor in the breast. Results We identified 17 pathological responders and 16 non-responders. PET/CT had lower specificity and accuracy, but higher sensitivity than MRI, although no significant difference was found between PET/CT and MRI. Following NAC, there were significant differences between pathological responders and non-responders in SULpeak ( P < 0.001), MTV ( P < 0.001), 1D ( P = 0.0003), TV ( P = 0.038), Δ%SULpeak ( P = 0.001), Δ%MTV ( P < 0.001), Δ%1D ( P < 0.001), and Δ%TV ( P = 0.001). Conclusion PET/CT showed lower specificity and accuracy than MRI in evaluating responses to NAC, but both PET/CT and MRI parameters may have predictive value in distinguishing therapeutic responders and non-responders following NAC.
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Affiliation(s)
- Eun Kyoung Choi
- 1 Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ie Ryung Yoo
- 2 Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sung Hun Kim
- 2 Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sonya Youngju Park
- 3 Molecular Imaging Program, Department of Radiology, Stanford Hospital and Clinics, Stanford, USA
| | - Joo Hyun O
- 2 Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Bong Joo Kang
- 2 Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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Holli-Helenius K, Salminen A, Rinta-Kiikka I, Koskivuo I, Brück N, Boström P, Parkkola R. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study. BMC Med Imaging 2017; 17:69. [PMID: 29284425 PMCID: PMC5747252 DOI: 10.1186/s12880-017-0239-z] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 12/15/2017] [Indexed: 12/23/2022] Open
Abstract
Background The aim of this study was to use texture analysis (TA) of breast magnetic resonance (MR) images to assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes. Methods Twenty-seven patients with histopathologically proven invasive ductal breast cancer were selected in preliminary study. Tumors were classified into molecular subtypes: luminal A (ER-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67 < 20 and low grade (I)) and luminal B (ER-positive and/or PR-positive, HER2-positive or HER2-negative with high Ki-67 ≥ 20 and higher grade (II or III)). Co-occurrence matrix -based texture features were extracted from each tumor on T1-weighted non fat saturated pre- and postcontrast MR images using TA software MaZda. Texture parameters and tumour volumes were correlated with tumour prognostic factors. Results Textural differences were observed mainly in precontrast images. The two most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (p = 0.003). The AUCs were 0.828 for sum entropy (p = 0.004), and 0.833 for sum variance (p = 0.003), and 0.878 for the model combining texture features sum entropy, sum variance (p = 0.001). In the LOOCV, the AUC for model combining features sum entropy and sum variance was 0.876. Sum entropy and sum variance showed positive correlation with higher Ki-67 index. Luminal B types were larger in volume and moderate correlation between larger tumour volume and higher Ki-67 index was also observed (r = 0.499, p = 0.008). Conclusions Texture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.
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Affiliation(s)
- Kirsi Holli-Helenius
- Department of Medical Physics, Medical Imaging Centre and Hospital Pharmacy, Pirkanmaa Hospital District, Post Box 2000, 33521, Tampere, Finland.
| | - Annukka Salminen
- Department of Radiology, Tampere University Hospital, Tampere, Finland
| | | | - Ilkka Koskivuo
- Department of Plastic and General Surgery Turku University Hospital, Turku, Finland
| | - Nina Brück
- Department of Plastic and General Surgery Turku University Hospital, Turku, Finland
| | - Pia Boström
- Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
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Olshen A, Wolf D, Jones EF, Newitt D, van ‘t Veer LJ, Yau C, Esserman L, Wulfkuhle JD, Gallagher RI, Singer L, Petricoin EF, Hylton N, Park CC. Features of MRI stromal enhancement with neoadjuvant chemotherapy: a subgroup analysis of the ACRIN 6657/I-SPY TRIAL. J Med Imaging (Bellingham) 2017; 5:011014. [PMID: 29296631 PMCID: PMC5741993 DOI: 10.1117/1.jmi.5.1.011014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 11/06/2017] [Indexed: 12/29/2022] Open
Abstract
Although the role of cancer-activated stroma in malignant progression has been well investigated, the influence of an activated stroma in therapy response is not well understood. Using retrospective pilot cohorts, we previously observed that MRI detected stromal contrast enhancement was associated with proximity to the tumor and was predictive for relapse-free survival in patients with breast cancer receiving neoadjuvant chemotherapy. Here, to evaluate the association of stromal contrast enhancement to therapy, we applied an advanced tissue mapping technique to evaluate stromal enhancement patterns within 71 patients enrolled in the I-SPY 1 neoadjuvant breast cancer trial. We correlated MR stromal measurements with stromal protein levels involved in tumor progression processes. We found that stromal percent enhancement values decrease with distance from the tumor edge with the estimated mean change ranging [Formula: see text] to [Formula: see text] ([Formula: see text]) for time points T2 through T4. While not statistically significant, we found a decreasing trend in global stromal signal enhancement ratio values with the use of chemotherapy. There were no statistically significant differences between MR enhancement measurements and stromal protein levels. Findings from this study indicate that stromal features characterized by MRI are impacted by chemotherapy and may have predictive value in a larger study.
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Affiliation(s)
- Adam Olshen
- University of California San Francisco, Department of Biostatistics and Epidemiology, San Francisco, California, United States.,University of California San Francisco, UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, California, United States
| | - Denise Wolf
- University of California San Francisco, Department of Laboratory Medicine, San Francisco, California, United States
| | - Ella F Jones
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
| | - David Newitt
- University of California San Francisco, Department of Surgery, San Francisco, California, United States
| | - Laura J van ‘t Veer
- University of California San Francisco, Department of Laboratory Medicine, San Francisco, California, United States
| | - Christina Yau
- University of California San Francisco, Department of Surgery, San Francisco, California, United States
| | - Laura Esserman
- University of California San Francisco, Department of Surgery, San Francisco, California, United States
| | - Julia D Wulfkuhle
- George Mason University, Center for Applied Proteomics and Molecular Medicine, Manassas, Virginia, United States
| | - Rosa I Gallagher
- George Mason University, Center for Applied Proteomics and Molecular Medicine, Manassas, Virginia, United States
| | - Lisa Singer
- University of California San Francisco, Department of Radiation Oncology, San Francisco, California, United States
| | - Emanuel F Petricoin
- George Mason University, Center for Applied Proteomics and Molecular Medicine, Manassas, Virginia, United States
| | - Nola Hylton
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
| | - Catherine C Park
- University of California San Francisco, UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, California, United States.,University of California San Francisco, Department of Radiation Oncology, San Francisco, California, United States
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Kim HS, Shin MS, Kim CJ, Yoo SH, Yoo TK, Eom YH, Chae BJ, Song BJ. Improved Model for Predicting Axillary Response to Neoadjuvant Chemotherapy in Patients with Clinically Node-Positive Breast Cancer. J Breast Cancer 2017; 20:378-385. [PMID: 29285043 PMCID: PMC5743998 DOI: 10.4048/jbc.2017.20.4.378] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 10/02/2017] [Indexed: 02/06/2023] Open
Abstract
Purpose Pathological complete response (pCR) of axillary lymph node (LN) is frequently achieved in patients with clinically node-positive breast cancer after neoadjuvant chemotherapy (NAC). Treatment of the axilla after NAC is not well established and the value of sentinel LN biopsy following NAC remains unclear. This study investigated the predictive value of axillary response following NAC and evaluated the predictive value of a model based on axillary response. Methods Data prospectively collected on 201 patients with clinically node-positive breast cancer who were treated with NAC and underwent axillary LN dissection (ALND) were retrieved. A model predictive of axillary pCR was developed based on clinicopathologic variables. The overall predictive ability between models was compared by receiver operating characteristic (ROC) curve analysis. Results Of 201 patients who underwent ALND after NAC, 68 (33.8%) achieved axillary pCR. Multivariate analysis using axillary LN pCR after NAC as the dependent variable showed that higher histologic grade (p=0.031; odds ratio [OR], 2.537; 95% confidence interval [CI], 1.087–5.925) and tumor response rate ≥47.1% (p=0.001; OR, 3.212; 95% CI, 1.584–6.515) were significantly associated with an increased probability of achieving axillary pCR. The area under the ROC curve for estimating axillary pCR was significantly higher in the model that included tumor response rate than in the model that excluded this rate (0.732 vs. 0.649, p=0.022). Conclusion Tumor response rate was the most significant independent predictor of axillary pCR in response to NAC. The model that included tumor response rate was a significantly better predictor of axillary pCR than the model that excluded tumor response rate.
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Affiliation(s)
- Hyung Suk Kim
- Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Man Sik Shin
- Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Chang Jong Kim
- Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sun Hyung Yoo
- Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Tae Kyung Yoo
- Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yong Hwa Eom
- Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Byung Joo Chae
- Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Byung Joo Song
- Division of Breast Surgery, Department of Surgery, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Korea
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Henderson S, Purdie C, Michie C, Evans A, Lerski R, Johnston M, Vinnicombe S, Thompson AM. Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer. Eur Radiol 2017; 27:4602-4611. [PMID: 28523352 PMCID: PMC5635097 DOI: 10.1007/s00330-017-4850-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 04/05/2017] [Accepted: 04/11/2017] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To investigate whether interim changes in hetereogeneity (measured using entropy features) on MRI were associated with pathological residual cancer burden (RCB) at final surgery in patients receiving neoadjuvant chemotherapy (NAC) for primary breast cancer. METHODS This was a retrospective study of 88 consenting women (age: 30-79 years). Scanning was performed on a 3.0 T MRI scanner prior to NAC (baseline) and after 2-3 cycles of treatment (interim). Entropy was derived from the grey-level co-occurrence matrix, on slice-matched baseline/interim T2-weighted images. Response, assessed using RCB score on surgically resected specimens, was compared statistically with entropy/heterogeneity changes and ROC analysis performed. Association of pCR within each tumour immunophenotype was evaluated. RESULTS Mean entropy percent differences between examinations, by response category, were: pCR: 32.8%, RCB-I: 10.5%, RCB-II: 9.7% and RCB-III: 3.0%. Association of ultimate pCR with coarse entropy changes between baseline/interim MRI across all lesions yielded 85.2% accuracy (area under ROC curve: 0.845). Excellent sensitivity/specificity was obtained for pCR prediction within each immunophenotype: ER+: 100%/100%; HER2+: 83.3%/95.7%, TNBC: 87.5%/80.0%. CONCLUSIONS Lesion T2 heterogeneity changes are associated with response to NAC using RCB scores, particularly for pCR, and can be useful across all immunophenotypes with good diagnostic accuracy. KEY POINTS • Texture analysis provides a means of measuring lesion heterogeneity on MRI images. • Heterogeneity changes between baseline/interim MRI can be linked with ultimate pathological response. • Heterogeneity changes give good diagnostic accuracy of pCR response across all immunophenotypes. • Percentage reduction in heterogeneity is associated with pCR with good accuracy and NPV.
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Affiliation(s)
- Shelley Henderson
- Department of Medical Physics, Ninewells Hospital and Medical School, Dundee, UK, DD1 9SY.
| | - Colin Purdie
- Department of Pathology, Ninewells Hospital and Medical School, Dundee, UK, DD1 9SY
| | - Caroline Michie
- Department of Oncology, Ninewells Hospital and Medical School, Dundee, UK, DD1 9SY
| | - Andrew Evans
- Division of Imaging and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK, DD1 9SY
| | - Richard Lerski
- Department of Medical Physics, Ninewells Hospital and Medical School, Dundee, UK, DD1 9SY
| | - Marilyn Johnston
- Department of Clinical Radiology, Ninewells Hospital and Medical School, Dundee, UK, DD1 9SY
| | - Sarah Vinnicombe
- Division of Imaging and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK, DD1 9SY
| | - Alastair M Thompson
- Department of Breast Surgical Oncology, University of Texas MD Anderson Cancer Centre, Houston, TX, 77030, USA
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Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom. Magn Reson Med 2017; 79:48-61. [DOI: 10.1002/mrm.26982] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 09/01/2017] [Accepted: 10/04/2017] [Indexed: 01/16/2023]
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Pöhlmann STL, Lim YY, Harkness E, Pritchard S, Taylor CJ, Astley SM. Three-dimensional segmentation of breast masses from digital breast tomosynthesis images. J Med Imaging (Bellingham) 2017; 4:034007. [PMID: 28948195 DOI: 10.1117/1.jmi.4.3.034007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 08/16/2017] [Indexed: 11/14/2022] Open
Abstract
Assessment of three-dimensional (3-D) morphology and volume of breast masses is important for cancer diagnosis, staging, and treatment but cannot be derived from conventional mammography. Digital breast tomosynthesis (DBT) provides data from which 3-D mass segmentation could be obtained. Our method combined Gaussian mixture models based on intensity and a texture measure indicative of in-focus structure, gray-level variance. Thresholding these voxel probabilities, weighted by distance to the estimated mass center, gave the final 3-D segmentation. Evaluation used 40 masses annotated twice by a consultant radiologist on in-focus slices in two diagnostic views. Human intraobserver variability was assessed as the overlap between repeated annotations (median 77% and range 25% to 91%). Comparing the segmented mass outline with probability-weighted ground truth from these annotations, median agreement was 68%, and range was 7% to 88%. Annotated and segmented diameters correlated well with histological mass size (both Spearman's rank correlations [Formula: see text]). The volumetric segmentation demonstrated better agreement with tumor volumes estimated from pathology than volume derived from radiological annotations (95% limits of agreement [Formula: see text] to 11 ml and [Formula: see text] to 41 ml, respectively). We conclude that it is feasible to assess 3-D mass morphology and volume from DBT, and the method has the potential to aid breast cancer management.
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Affiliation(s)
- Stefanie T L Pöhlmann
- University of Manchester, Division of Informatics Imaging and Data Sciences, School of Health Sciences, Faculty of Biology Medicine and Health, Manchester, United Kingdom
| | - Yit Y Lim
- University Hospital of South Manchester, Nightingale Breast Centre, Manchester, United Kingdom
| | - Elaine Harkness
- University of Manchester, Division of Informatics Imaging and Data Sciences, School of Health Sciences, Faculty of Biology Medicine and Health, Manchester, United Kingdom
| | - Susan Pritchard
- University Hospital of South Manchester, Nightingale Breast Centre, Manchester, United Kingdom
| | - Christopher J Taylor
- University of Manchester, Division of Informatics Imaging and Data Sciences, School of Health Sciences, Faculty of Biology Medicine and Health, Manchester, United Kingdom
| | - Susan M Astley
- University of Manchester, Division of Informatics Imaging and Data Sciences, School of Health Sciences, Faculty of Biology Medicine and Health, Manchester, United Kingdom
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49
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Thermal tomography for monitoring tumor response to neoadjuvant chemotherapy in women with locally advanced breast cancer. Oncotarget 2017; 8:68974-68983. [PMID: 28978172 PMCID: PMC5620312 DOI: 10.18632/oncotarget.16569] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 03/15/2017] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND & AIMS This study aims to analyze the feasibility and predictive value of thermal tomography (TT) for monitoring early treatment response in patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC). METHODS Patients with LABC who were due to receive six cycles of NAC were examined by TT prior to NAC, the second cycle of NAC, the fourth cycle of NAC and surgery. Changes in TT parameters and ultrasonography were correlated with pathologic response to NAC, and the predictive value was assessed. RESULTS Forty-four patients were evaluable for response (25 pathologic responders and 19 nonresponders). As early as after the first cycle of NAC, changes in the TT parameters ΔTs, ΔTn, and ΔTa correlated significantly with pathologic response (P < 0.05). The best predictor of pathologic response after the 6th cycle of NAC was TT (area under the receiver operating characteristic curve, 0.794), as opposed to cross-sectional areas and the longest diameter by ultrasonography. CONCLUSIONS TT allows for monitoring early tumor response to NAC and can predict pathologic response in the early stages of therapy. Therefore, TT could be used as a novel imaging modality to monitor NAC treatment.
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Brandelik SC, Krzykalla J, Hielscher T, Hillengass J, Kloth JK, Kauczor HU, Weber MA. [Focal lesions in whole-body MRI in multiple myeloma : Quantification of tumor mass and correlation with disease-related parameters and prognosis]. Radiologe 2017; 58:72-78. [PMID: 28905085 DOI: 10.1007/s00117-017-0299-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND AND OBJECTIVES In this study, we evaluated methods of quantification of tumor mass in whole-body MRI (wb-MRI) in multiple myeloma and correlated these with disease-related parameters in serum and bone marrow. MATERIALS AND METHODS We retrospectively evaluated wb-MRIs of 52 patients with focal infiltration pattern and a total of 700 focal lesions (subsequently called lesions). We determined the longest diameter (LD), the segmented volume (SV), and the morphology (spherical or non-spherical). We correlated total number/volume of the lesions with clinical parameters and prognosis and furthermore LD with SV. After that we analyzed the agreement of SV and estimated volume (EV) using the volume formula of a sphere based on LD. RESULTS Results showed no significant correlations of total number/volume with prognosis or clinical parameters. The latter were situated predominantly in the normal range. Furthermore, 10% of lesions were spherical. SV and LD correlated significantly in single lesions and on patient level. SV was in lesions <6 cm3 systematically larger and in lesions ≥6 cm3 smaller than EV. In 95%, we found in small lesions a deviation of EV versus SV from +0.9 cm3 to -4.6 cm3 and in large lesions from +160 cm3 to -111 cm3 (EV-SV). CONCLUSIONS Quantification of tumor mass in the focal infiltration pattern is performed more accurately by volumetry than LD due to the predominant existence of non-spherical lesions. The patient cohort with clinical parameters predominantly in the normal range is distributed to ISS stage I and partly pretreated, a fact that makes interpretation of absent correlations more difficult. Consider also a variation in activitiy of lesions and a diffuse infiltration not detectable by MRI.
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Affiliation(s)
- S C Brandelik
- Diagnostische und Interventionelle Radiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Deutschland.
| | - J Krzykalla
- Biostatistik, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
| | - T Hielscher
- Biostatistik, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
| | - J Hillengass
- Hämatologie und Onkologie, Universitätsklinikum Heidelberg, Heidelberg, Deutschland
| | - J K Kloth
- Radiologie Löbau, Löbau, Deutschland
| | - H U Kauczor
- Diagnostische und Interventionelle Radiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Deutschland
| | - M A Weber
- Diagnostische und Interventionelle Radiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Deutschland
- Diagnostische und Interventionelle Radiologie, Universitätsmedizin Rostock, Rostock, Deutschland
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