Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score® Test?
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. MRI Protocol
- Axial pre-contrast 2D FSE T2-weighted fat-suppressed sequence based on a three-point Dixon technique (IDEAL): repetition time (RT) = 11,000 ms, echo time (ET) 119 ms, echo train length (ETL) = 19, bandwidth = 62.5 kHz, matrix = 512 × 224, thickness = 3–5 mm, interval = 0.1, field of view (FOV) = 350 × 350 mm, number of excitation (NEX) = 1, scan time = 130 s.
- Axial pre-contrast diffusion-weighted echo-planar imaging (DWI-EPI) sequence: RT = 4983 ms, ET = 58 ms, bandwidth = 250 kHz, matrix = 150 × 150, slice thickness = 3–5 mm, FOV = 350 × 350 mm, NEX = 2-2-4, scan time = 230 s; DWI-EPI sequences comprised b-values of 0, 500 and 1000 s/mm2 and the corresponding apparent diffusion coefficient (ADC) maps were calculated automatically.
- Axial dynamic 3D spoiled GE T1-weighted fat-suppressed sequences (DISCO), based on a two-point Dixon fat-water reconstruction algorithm: flip angle = 15°, RT = 4 ms, ET = 2 ms, bandwidth = 166.7 kHz, matrix = 320 × 320, slice thickness = 1.40 mm, FOV = 340 × 340 mm, NEX = 1, performed before and 9 times after contrast agent administration (total scan time = 363 s).
- Sagittal 3D spoiled GE post-contrast T1-weighted sequence.
2.3. MR Imaging Evaluation
2.4. Histopathological Analysis
2.5. Oncotype DX Breast Recurrence Score® Test
2.6. Combined Onco-Radiologic Assessment
2.7. Statistical Analysis
3. Results
3.1. Patient and Tumor Characteristics
3.2. MRI
3.2.1. MRI-Derived and Pathological Features vs. ODXRS
3.2.2. MRI-Derived and Pathological Features vs. Stratified ODXRS
3.2.3. MRI vs. Post-ODXRS Assay Therapeutic Decision
3.3. Combined Onco-Radiologic Assessment
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
- Goldhirsch, A.; Winer, E.P.; Coates, A.S.; Gelber, R.D.; Piccart-Gebhart, M.; Thürlimann, B.; Senn, H.J.; Panel Members. Personalizing the treatment of women with early breast cancer: Highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann. Oncol. 2013, 24, 2206–2223. [Google Scholar] [CrossRef] [PubMed]
- National Cancer Institute. Cancer Stat Facts: Female Breast Cancer Subtypes. Available online: https://seer.cancer.gov/statfacts/html/breast-subtypes.html (accessed on 30 June 2022).
- American Cancer Society—Cancer Statistics Center, Breast Cancer Facts and Figures. Available online: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/breast-cancer-facts-and-figures/breast-cancer-facts-and-figures-2019-2020.pdf (accessed on 30 June 2022).
- Galati, F.; Moffa, G.; Pediconi, F. Breast imaging: Beyond the detection. Eur. J. Radiol. 2022, 146, 110051. [Google Scholar] [CrossRef] [PubMed]
- Mann, R.M.; Balleyguier, C.; Baltzer, P.A.; Bick, U.; Colin, C.; Cornford, E.; Evans, A.; Fallenberg, E.; Forrai, G.; Fuchsjäger, M.H.; et al. Breast MRI: EUSOBI recommendations for women’s information. Eur. Radiol. 2015, 25, 3669–3678. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sardanelli, F.; Boetes, C.; Borisch, B.; Decker, T.; Federico, M.; Gilbert, F.J.; Helbich, T.; Heywang-Köbrunner, S.H.; Kaiser, W.A.; Kerin, M.J.; et al. Magnetic resonance imaging of the breast: Recommendations from the EUSOMA working group. Eur. J. Cancer 2010, 46, 1296–1316. [Google Scholar] [CrossRef]
- Galati, F.; Rizzo, V.; Trimboli, R.M.; Kripa, E.; Maroncelli, R.; Pediconi, F. MRI as a biomarker for breast cancer diagnosis and prognosis. BJR Open 2022, 4, 20220002. [Google Scholar] [CrossRef]
- Galati, F.; Rizzo, V.; Moffa, G.; Caramanico, C.; Kripa, E.; Cerbelli, B.; D’Amati, G.; Pediconi, F. Radiologic-pathologic correlation in breast cancer: Do MRI biomarkers correlate with pathologic features and molecular subtypes? Eur. Radiol. Exp. 2022, 6, 39. [Google Scholar] [CrossRef]
- Moffa, G.; Galati, F.; Collalunga, E.; Rizzo, V.; Kripa, E.; D’Amati, G.; Pediconi, F. Can MRI Biomarkers Predict Triple-Negative Breast Cancer? Diagnostics 2020, 10, 1090. [Google Scholar] [CrossRef]
- Tsarouchi, M.I.; Vlachopoulos, G.F.; Karahaliou, A.N.; Vassiou, K.G.; Costaridou, L.I. Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis. Phys. Med. 2020, 80, 101–110. [Google Scholar] [CrossRef]
- Panzironi, G.; Moffa, G.; Galati, F.; Marzocca, F.; Rizzo, V.; Pediconi, F. Peritumoral edema as a biomarker of the aggressiveness of breast cancer: Results of a retrospective study on a 3 T scanner. Breast Cancer Res. Treat. 2020, 18, 53–60. [Google Scholar] [CrossRef]
- Galati, F.; Luciani, M.L.; Caramanico, C.; Moffa, G.; Catalano, C.; Pediconi, F. Breast Magnetic Resonance Spectroscopy at 3 T in Biopsy-Proven Breast Cancers: Does Choline Peak Correlate with Prognostic Factors? Investig. Radiol. 2019, 54, 767–773. [Google Scholar] [CrossRef] [PubMed]
- Tan, W.; Yang, M.; Yang, H.; Zhou, F.; Shen, W. Predicting the response to neoadjuvant therapy for early-stage breast cancer: Tumor-, blood-, and imaging-related biomarkers. Cancer Manag. Res. 2018, 10, 4333–4347. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rahbar, H.; Partridge, S.C. Multiparametric MR imaging of breast cancer. Magn. Reson. Imaging Clin. N. Am. 2016, 24, 223–238. [Google Scholar] [CrossRef] [Green Version]
- National Cancer Institute. SEER*Explorer Application. Breast: Recent Trends in SEER Age-Adjusted Incidence Rates, 2004–2019. Available online: https://seer.cancer.gov/statistics-network/explorer/application.html?site=55&data_type=1&graph_type=2&compareBy=stage&chk_stage_104=104&chk_stage_105=105&chk_stage_106=106&chk_stage_107=107&hdn_rate_type=1&sex=3&race=1&age_range=1&advopt_precision=1&advopt_show_ci=on&hdn_view=1&advopt_display=1#tableWrap (accessed on 30 June 2022).
- Early Breast Cancer Trialists’ Collaborative Group (EBCTCG); Davies, C.; Godwin, J.; Gray, R.; Clarke, M.; Cutter, D.; Darby, S.; McGale, P.; Pan, H.C.; Taylor, C.; et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: Patient-level meta-analysis of randomised trials. Lancet 2011, 378, 771–784. [Google Scholar] [CrossRef] [Green Version]
- Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 2005, 365, 1687–1717. [Google Scholar] [CrossRef]
- Early Breast Cancer Trialists’ Collaborative Group (EBCTCG); Peto, R.; Davies, C.; Godwin, J.; Gray, R.; Pan, H.C.; Clarke, M.; Cutter, D.; Darby, S.; McGale, P.; et al. Comparisons between different polychemotherapy regimens for early breast cancer: Meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 2012, 379, 432–444. [Google Scholar] [CrossRef] [Green Version]
- Sparano, J.A.; Paik, S. Development of the 21-gene assay and its application in clinical practice and clinical trials. J. Clin. Oncol. 2008, 26, 721–728. [Google Scholar] [CrossRef] [PubMed]
- Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E., Jr.; Dees, E.C.; Goetz, M.P.; Olson, J.A., Jr.; et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. N. Engl. J. Med. 2018, 379, 111–121. [Google Scholar] [CrossRef] [Green Version]
- Andre, F.; Ismaila, N.; Henry, N.L.; Somerfield, M.R.; Bast, R.C.; Barlow, W.; Collyar, D.E.; Hammond, M.E.; Kuderer, N.M.; Liu, M.C.; et al. Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women with Early-Stage Invasive Breast Cancer: ASCO Clinical Practice Guideline Update-Integration of Results from TAILORx. J. Clin. Oncol. 2019, 37, 1956–1964. [Google Scholar] [CrossRef] [Green Version]
- Nitz, U.; Gluz, O.; Christgen, M.; Kates, R.E.; Clemens, M.; Malter, W.; Nuding, B.; Aktas, B.; Kuemmel, S.; Reimer, T.; et al. Reducing chemotherapy use in clinically high-risk, genomically low-risk pN0 and pN1 early breast cancer patients: Five-year data from the prospective, randomised phase 3 West German Study Group (WSG) PlanB trial. Breast Cancer Res. Treat. 2017, 165, 573–583. [Google Scholar] [CrossRef]
- Moran, M.S.; Abraham, J.; Aft, R.; Agnese, D.; Allison, K.H.; Anderson, B.; Burstein, H.J.; Chew, H.; Dang, C.; Elias, A.D.; et al. Breast Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Cancer Netw. 2022, 20, 691–722. Available online: https://jnccn.org/view/journals/jnccn/20/6/article-p691.xml (accessed on 31 July 2022).
- Associazione Italiana di Oncologia medica (AIOM)—Linee guida Neoplasie della Mammella, Edizione 2021. Available online: https://www.aiom.it/wp-content/uploads/2021/11/2021_LG_AIOM_Neoplasie_Mammella_11112021.pdf.pdf (accessed on 31 July 2022).
- Harris, L.N.; Ismaila, N.; McShane, L.M.; Andre, F.; Collyar, D.E.; Gonzalez-Angulo, A.M.; Hammond, E.H.; Kuderer, N.M.; Liu, M.C.; Mennel, R.G.; et al. Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women with Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline. J. Clin. Oncol. 2016, 34, 1134–1150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Henry, N.L.; Somerfield, M.R.; Abramson, V.G.; Allison, K.H.; Anders, C.K.; Chingos, D.T.; Hurria, A.; Openshaw, T.H.; Krop, I.E. Role of Patient and Disease Factors in Adjuvant Systemic Therapy Decision Making for Early-Stage, Operable Breast Cancer: American Society of Clinical Oncology Endorsement of Cancer Care Ontario Guideline Recommendations. J. Clin. Oncol. 2016, 34, 2303–2311. [Google Scholar] [CrossRef] [PubMed]
- Morris, E.A.; Comstock, C.E.; Lee, C.H. ACR BI-RADS magnetic resonance imaging. In ACR BI-RADS Atlas, Breast Imaging Reporting and Data System, 5th ed.; American College of Radiology: Reston, VA, USA, 2013. [Google Scholar]
- Wolff, A.C.; Hammond, M.E.; Hicks, D.G.; Dowsett, M.; McShane, L.M.; Allison, K.H.; Allred, D.C.; Bartlett, J.M.; Bilous, M.; Fitzgibbons, P.; et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J. Clin. Oncol. 2013, 31, 3997–4013. [Google Scholar] [CrossRef]
- Giuliano, A.E.; Connolly, J.L.; Edge, S.B.; Mittendorf, E.A.; Rugo, H.S.; Solin, L.J.; Weaver, D.L.; Winchester, D.J.; Hortobagyi, G.N. Breast Cancer-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J. Clin. 2017, 67, 290–303. [Google Scholar] [CrossRef] [Green Version]
- Lewin, R.; Sulkes, A.; Shochat, T.; Tsoref, D.; Rizel, S.; Liebermann, N.; Hendler, D.; Neiman, V.; Ben-Aharon, I.; Friedman, E.; et al. Oncotype-DX recurrence score distribution in breast cancer patients with BRCA1/2 mutations. Breast Cancer Res. Treat. 2016, 157, 511–516. [Google Scholar] [CrossRef]
- Stiggelbout, A.M.; de Haes, J.C.; van de Velde, C.J. Adjuvant chemotherapy in node negative breast cancer: Patterns of use and oncologists’ preferences. Ann. Oncol. 2000, 11, 631–633. [Google Scholar] [CrossRef] [PubMed]
- Dieci, M.V.; Guarneri, V.; Zustovich, F.; Mion, M.; Morandi, P.; Bria, E.; Merlini, L.; Bullian, P.; Oliani, C.; Gori, S.; et al. Impact of 21-Gene Breast Cancer Assay on Treatment Decision for Patients with T1-T3, N0-N1, Estrogen Receptor-Positive/Human Epidermal Growth Receptor 2-Negative Breast Cancer: Final Results of the Prospective Multicenter ROXANE Study. Oncologist 2019, 24, 1424–1431. [Google Scholar] [CrossRef] [Green Version]
- Wu, J.; Fang, Y.; Lin, L.; Fei, X.; Gao, W.; Zhu, S.; Zong, Y.; Chen, X.; Huang, O.; He, J.; et al. Distribution patterns of 21-gene recurrence score in 980 Chinese estrogen receptor-positive, HER2-negative early breast cancer patients. Oncotarget 2017, 13, 38706–38716. [Google Scholar] [CrossRef] [Green Version]
- Zhang, M.; Sadinski, M.; Haddad, D.; Bae, M.S.; Martinez, D.; Morris, E.A.; Gibbs, P.; Sutton, E.J. Background Parenchymal Enhancement on Breast MRI as a Prognostic Surrogate: Correlation with Breast Cancer Oncotype Dx Score. Front. Oncol. 2021, 10, 595820. [Google Scholar] [CrossRef]
- Kim, H.J.; Choi, W.J.; Kim, H.H.; Cha, J.H.; Shin, H.J.; Chae, E.Y. Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer. Clin. Imaging 2021, 75, 131–137. [Google Scholar] [CrossRef] [PubMed]
- Woodard, G.A.; Ray, K.M.; Joe, B.N.; Price, E.R. Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features. Radiology 2018, 286, 60–70. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dialani, V.; Gaur, S.; Mehta, T.S.; Venkataraman, S.; Fein-Zachary, V.; Phillips, J.; Brook, A.; Slanetz, P.J. Prediction of Low versus High Recurrence Scores in Estrogen Receptor-Positive, Lymph Node-Negative Invasive Breast Cancer on the Basis of Radiologic-Pathologic Features: Comparison with Oncotype DX Test Recurrence Scores. Radiology 2016, 280, 370–378. [Google Scholar] [CrossRef] [Green Version]
- Grimm, L.J.; Zhang, J.; Baker, J.A.; Soo, M.S.; Johnson, K.S.; Mazurowski, M.A. Relationships between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype. Breast J. 2017, 23, 579–582. [Google Scholar] [CrossRef] [PubMed]
- Kim, J.Y.; Kim, J.J.; Hwangbo, L.; Lee, J.W.; Lee, N.K.; Nam, K.J.; Choo, K.S.; Kang, T.; Park, H.; Son, Y.; et al. Diffusion-weighted MRI of estrogen receptor-positive, HER2-negative, node-negative breast cancer: Association between intratumoral heterogeneity and recurrence risk. Eur. Radiol. 2020, 30, 66–76. [Google Scholar] [CrossRef]
- Roknsharifi, S.; Fishman, M.D.C.; Agarwal, M.D.; Brook, A.; Kharbanda, V.; Dialani, V. The role of diffusion weighted imaging as supplement to dynamic contrast enhanced breast MRI: Can it help predict malignancy, histologic grade and recurrence? Acad. Radiol. 2019, 26, 923–929. [Google Scholar] [CrossRef]
- Thakur, S.B.; Durando, M.; Milans, S.; Cho, G.Y.; Gennaro, L.; Sutton, E.J.; Giri, D.; Morris, E.A. Apparent diffusion coefficient in estrogen receptor-positive and lymph node-negative invasive breast cancers at 3.0T DW-MRI: A potential predictor for an oncotype Dx test recurrence score. J. Magn. Reson. Imaging 2018, 47, 401–409. [Google Scholar] [CrossRef] [PubMed]
- Amornsiripanitch, N.; Nguyen, V.T.; Rahbar, H.; Hippe, D.S.; Gadi, V.K.; Rendi, M.H.; Partridge, S.C. Diffusion-weighted MRI characteristics associated with prognostic pathological factors and recurrence risk in invasive ER+/HER2- breast cancers. J. Magn. Reson. Imaging 2018, 48, 226–236. [Google Scholar] [CrossRef]
- Ha, R.; Chang, P.; Mutasa, S.; Karcich, J.; Goodman, S.; Blum, E.; Kalinsky, K.; Liu, M.Z.; Jambawalikar, S. Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score. J. Magn. Reson. Imaging 2019, 49, 518–524. [Google Scholar] [CrossRef]
- Nam, K.J.; Park, H.; Ko, E.S.; Lim, Y.; Cho, H.H.; Lee, J.E. Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores. Medicine 2019, 98, e15871. [Google Scholar] [CrossRef]
- Saha, A.; Harowicz, M.R.; Wang, W.; Mazurowski, M.A. A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. J. Cancer Res. Clin. Oncol. 2018, 799–807. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Zhu, Y.; Burnside, E.S.; Drukker, K.; Hoadley, K.A.; Fan, C.; Conzen, S.D.; Whitman, G.J.; Sutton, E.J.; Net, J.M.; et al. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology 2016, 281, 382–391. [Google Scholar] [CrossRef] [PubMed]
MRI Features | Reader 1 | Reader 2 |
---|---|---|
Size (mm) | ||
Mean (range) | 20.38 (3–75) | 20.19 (3–80) |
BI-RADS category | ||
4 | 24/58 | 28/58 |
5 | 34/58 | 30/58 |
T2 signal | ||
Hypointensity | 28/58 | 25/58 |
Hyperintensity | 13/58 | 12/58 |
Isointensity | 16/58 | 20/58 |
Not evaluable | 1/58 | 1/58 |
Intralesional necrosis | ||
Absent | 55/58 | 55/58 |
Present | 2/58 | 2/58 |
Not evaluable | 1/58 | 1/58 |
Peritumoral edema | ||
Absent | 41/58 | 41/58 |
Present | 16/58 | 16/58 |
Not evaluable | 1/58 | 1/58 |
Mass | 47/58 | 47/58 |
Non-mass enhancement (NME) | 11/58 | 11/58 |
Masses: Shape | ||
Round | 15/47 | 15/47 |
Oval | 13/47 | 12/47 |
Irregular | 19/47 | 20/47 |
Masses: Margins | ||
Circumscribed | 11/47 | 10/47 |
Irregular | 24/47 | 22/47 |
Spiculated | 12/47 | 15/47 |
Masses: Internal enhancement characteristics | ||
Homogeneous | 16/47 | 14/47 |
Non Homogeneous | 31/47 | 33/47 |
NME: Distribution | ||
Focal | 1/11 | 0/11 |
Linear | 0/11 | 1/11 |
Segmental | 3/11 | 3/11 |
Regional | 3/11 | 3/11 |
Multiple regions | 4/11 | 4/11 |
NME: Internal enhancement patterns | ||
Heterogeneous | 4/11 | 3/11 |
Clumped | 7/11 | 8/11 |
DWI signal | ||
Hypointensity | 29/58 | 28/58 |
Hyperintensity | 1/58 | 2/58 |
Isointensity | 2/58 | 2/58 |
Not evaluable | 26/58 | 26/58 |
ADC value (10−6 mm2/s) | ||
Mean (range) | 990.04 (586–1450) | 996.00 (586–1450) |
Pathologic LN | ||
Absent | 47/58 | 48/58 |
Present | 11/58 | 10/58 |
Spearman’s Correlation Coefficient | Univariate Analysis | Multivariate Analysis (Backward Stepwise Regression) | ||||
---|---|---|---|---|---|---|
ρ | B (95% CI) * | B (95% CI) * | Reader 1 | Reader 2 | ||
B (95% CI) * | B (95% CI) * | |||||
Grading | 0.362 p = 0.005 | 5.52 (2.07–8.97) p = 0.002 | 4.13 (0.73–7.53) p = 0.018 | 4.07 (0.78–7.36) p = 0.016 | ||
pT | 0.441 p = 0.001 | 5.78 (1.79–9.78) p = 0.005 | 4.06 (0.18–7.94) p = 0.041 | 3.40 (−0.39–7.18) p = 0.077 | ||
Lesion size | Reader 1 | Reader 2 | Reader 1 | Reader 2 | ||
0.388 p = 0.003 | 0.389 p = 0.003 | 0.15 (0.01–0.28) p = 0.035 | 0.14 (0.01–0.27) p = 0.033 | Eliminated | Eliminated | |
BI-RADS | Reader 1 | Reader 2 | Reader 1 | Reader 2 | ||
0.314 p = 0.016 | 0.440 p = 0.001 | 5.19 (0.83–9.55) p = 0.02 | 7.00 (2.9–11.11) p = 0.001 | 4.54 (0.59–8.49) p = 0.025 | 5.87 (2.06–9.68) p = 0.003 |
Low (0–15) | Intermediate (16–25) | High (26–100) | p-Value | |||||
---|---|---|---|---|---|---|---|---|
Number of Patients | 35 | 16 | 7 | |||||
Grading | 0.058 | |||||||
1 | 5 | 2 | 0 | |||||
2 | 24 | 10 | 2 | |||||
3 | 6 | 4 | 5 | |||||
pT | 0.045 | |||||||
1 | 26 | 7 | 2 | |||||
2 | 9 | 8 | 5 | |||||
3 | 0 | 1 | 0 | |||||
MRI features | Reader 1 | Reader 2 | Reader 1 | Reader 2 | Reader 1 | Reader 2 | Reader 1 | Reader 2 |
BI-RADS category | 0.043 | 0.004 | ||||||
4 | 19 | 23 | 4 | 4 | 1 | 1 | ||
5 | 16 | 12 | 12 | 12 | 6 | 6 | ||
T2 signal | 0.591 | 0.058 | ||||||
Hypointensity | 17 | 15 | 9 | 10 | 2 | 0 | ||
Hyperintensity | 8 | 8 | 2 | 2 | 3 | 2 | ||
Isointensity | 10 | 12 | 4 | 3 | 2 | 5 | ||
Not evaluable | 0 | 0 | 1 | 1 | 0 | 0 | ||
Enhancement | 0.118 | 0.118 | ||||||
Mass | 31 | 31 | 12 | 12 | 4 | 4 | ||
NME | 4 | 4 | 4 | 4 | 3 | 3 | ||
Masses: Internal enhancement characteristics | 0.002 | 0.026 | ||||||
Homogeneous | 16 | 13 | 0 | 0 | 0 | 1 | ||
Non homogeneous | 15 | 18 | 12 | 12 | 4 | 3 | ||
Pathologic LN | 0.118 | 0.344 | ||||||
Present | 4 | 4 | 4 | 4 | 3 | 2 | ||
Absent | 31 | 31 | 12 | 12 | 4 | 5 |
χ2 Test | Univariate Logistic Regression | Multivariate Logistic Regression | ||||
---|---|---|---|---|---|---|
p-Value | OR (95% CI) * | OR (95% CI) * | OR (95% CI) * | OR (95% CI) * | ||
Grading | 0.082 | |||||
pT | 0.326 | |||||
MRI features | Reader 1 | Reader 2 | Reader 1 | Reader 2 | Reader 1 | Reader 2 |
BI-RADS | 0.002 | <0.001 | 16.1 (1.94–133.52) p = 0.010 | 23.63 (2.83–197.00) p = 0.003 | 48.00 (4.90–469.60) p = 0.001 | |
T2 signal (Hypointensity vs. Non hypointensity) | 0.154 | 0.03 | 4.40 (1.08–17.89) p = 0.038 | 10.57 (1.95–57.19) p = 0.006 | ||
Mass enhancement (Homogeneous vs. Non homogeneous) | 0.006 | 0.086 | Out of scale | |||
Pathologic LN | 0.099 | 0.262 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Galati, F.; Magri, V.; Moffa, G.; Rizzo, V.; Botticelli, A.; Cortesi, E.; Pediconi, F. Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score® Test? Diagnostics 2022, 12, 2730. https://doi.org/10.3390/diagnostics12112730
Galati F, Magri V, Moffa G, Rizzo V, Botticelli A, Cortesi E, Pediconi F. Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score® Test? Diagnostics. 2022; 12(11):2730. https://doi.org/10.3390/diagnostics12112730
Chicago/Turabian StyleGalati, Francesca, Valentina Magri, Giuliana Moffa, Veronica Rizzo, Andrea Botticelli, Enrico Cortesi, and Federica Pediconi. 2022. "Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score® Test?" Diagnostics 12, no. 11: 2730. https://doi.org/10.3390/diagnostics12112730
APA StyleGalati, F., Magri, V., Moffa, G., Rizzo, V., Botticelli, A., Cortesi, E., & Pediconi, F. (2022). Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score® Test? Diagnostics, 12(11), 2730. https://doi.org/10.3390/diagnostics12112730