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Curcio C, Mucciolo G, Roux C, Brugiapaglia S, Scagliotti A, Guadagnin G, Conti L, Longo D, Grosso D, Papotti MG, Hirsch E, Cappello P, Varner JA, Novelli F. PI3Kγ inhibition combined with DNA vaccination unleashes a B-cell-dependent antitumor immunity that hampers pancreatic cancer. J Exp Clin Cancer Res 2024; 43:157. [PMID: 38824552 PMCID: PMC11143614 DOI: 10.1186/s13046-024-03080-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 05/24/2024] [Indexed: 06/03/2024] Open
Abstract
Phosphoinositide-3-kinase γ (PI3Kγ) plays a critical role in pancreatic ductal adenocarcinoma (PDA) by driving the recruitment of myeloid-derived suppressor cells (MDSC) into tumor tissues, leading to tumor growth and metastasis. MDSC also impair the efficacy of immunotherapy. In this study we verify the hypothesis that MDSC targeting, via PI3Kγ inhibition, synergizes with α-enolase (ENO1) DNA vaccination in counteracting tumor growth.Mice that received ENO1 vaccination followed by PI3Kγ inhibition had significantly smaller tumors compared to those treated with ENO1 alone or the control group, and correlated with i) increased circulating anti-ENO1 specific IgG and IFNγ secretion by T cells, ii) increased tumor infiltration of CD8+ T cells and M1-like macrophages, as well as up-modulation of T cell activation and M1-like related transcripts, iii) decreased infiltration of Treg FoxP3+ T cells, endothelial cells and pericytes, and down-modulation of the stromal compartment and T cell exhaustion gene transcription, iv) reduction of mature and neo-formed vessels, v) increased follicular helper T cell activation and vi) increased "antigen spreading", as many other tumor-associated antigens were recognized by IgG2c "cytotoxic" antibodies. PDA mouse models genetically devoid of PI3Kγ showed an increased survival and a pattern of transcripts in the tumor area similar to that of pharmacologically-inhibited PI3Kγ-proficient mice. Notably, tumor reduction was abrogated in ENO1 + PI3Kγ inhibition-treated mice in which B cells were depleted.These data highlight a novel role of PI3Kγ in B cell-dependent immunity, suggesting that PI3Kγ depletion strengthens the anti-tumor response elicited by the ENO1 DNA vaccine.
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Affiliation(s)
- Claudia Curcio
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
| | - Gianluca Mucciolo
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
| | - Cecilia Roux
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
| | - Silvia Brugiapaglia
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
| | - Alessandro Scagliotti
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
| | - Giorgia Guadagnin
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
| | - Laura Conti
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
- Molecular Biotechnology Center, University of Torino, Turin, Italy
| | - Dario Longo
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Turin, Italy
| | - Demis Grosso
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
| | - Mauro Giulio Papotti
- Pathology Unit, Department of Medical Sciences, University of Torino, AOU Città Della Salute E Della Scienza Di Torino, Turin, Italy
| | - Emilio Hirsch
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
- Molecular Biotechnology Center, University of Torino, Turin, Italy
| | - Paola Cappello
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy
- Molecular Biotechnology Center, University of Torino, Turin, Italy
| | - Judith A Varner
- Moores Cancer Center, Department of Pathology, University of California, San Diego, CA, USA
| | - Francesco Novelli
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Piazza Nizza 44Bis, 10126, Turin, Italy.
- Molecular Biotechnology Center, University of Torino, Turin, Italy.
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2
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Sun R, Wei L, Hou X, Chen Y, Han B, Xie Y, Nie S. Molecular-subtype guided automatic invasive breast cancer grading using dynamic contrast-enhanced MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107804. [PMID: 37716219 DOI: 10.1016/j.cmpb.2023.107804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 04/05/2023] [Accepted: 09/05/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Histological grade and molecular subtype have presented valuable references in assigning personalized or precision medicine as the significant prognostic indicators representing biological behaviors of invasive breast cancer (IBC). To evaluate a two-stage deep learning framework for IBC grading that incorporates with molecular-subtype (MS) information using DCE-MRI. METHODS In Stage I, an innovative neural network called IOS2-DA is developed, which includes a dense atrous-spatial pyramid pooling block with a pooling layer (DA) and inception-octconved blocks with double kernel squeeze-and-excitations (IOS2). This method focuses on the imaging manifestation of IBC grades and performs preliminary prediction using a novel class F1-score loss function. In Stage II, a MS attention branch is introduced to fine-tune the integrated deep vectors from IOS2-DA via Kullback-Leibler divergence. The MS-guided information is weighted with preliminary results to obtain classification values, which are analyzed by ensemble learning for tumor grade prediction on three MRI post-contrast series. Objective assessment is quantitatively evaluated by receiver operating characteristic curve analysis. DeLong test is applied to measure statistical significance (P < 0.05). RESULTS The molecular-subtype guided IOS2-DA performs significantly better than the single IOS2-DA in terms of accuracy (0.927), precision (0.942), AUC (0.927, 95% CI: [0.908, 0.946]), and F1-score (0.930). The gradient-weighted class activation maps show that the feature representations extracted from IOS2-DA are consistent with tumor areas. CONCLUSIONS IOS2-DA elucidates its potential in non-invasive tumor grade prediction. With respect to the correlation between MS and histological grade, it exhibits remarkable clinical prospects in the application of relevant clinical biomarkers to enhance the diagnostic effectiveness of IBC grading. Therefore, DCE-MRI tends to be a feasible imaging modality for the thorough preoperative assessment of breast biological behavior and carcinoma prognosis.
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Affiliation(s)
- Rong Sun
- School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai 200093, China
| | - Long Wei
- School of Computer Science and Technology, Shandong Jianzhu University, Shandong, China
| | - Xuewen Hou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai 200093, China
| | - Yang Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai 200093, China
| | - Baosan Han
- Department of General Surgery, Xinhua Hospital, Affiliated with Shanghai Jiao Tong University School of Medicine, China.
| | - Yuanzhong Xie
- Medical Imaging Center, Tai'an Central Hospital, No. 29 Long-Tan Road, Shandong 271099, China.
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai 200093, China.
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Frankowska K, Zarobkiewicz M, Dąbrowska I, Bojarska-Junak A. Tumor infiltrating lymphocytes and radiological picture of the tumor. Med Oncol 2023; 40:176. [PMID: 37178270 PMCID: PMC10182948 DOI: 10.1007/s12032-023-02036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023]
Abstract
Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.
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Affiliation(s)
- Karolina Frankowska
- Department of Clinical Immunology, Medical University of Lublin, Lublin, Poland
| | - Michał Zarobkiewicz
- Department of Clinical Immunology, Medical University of Lublin, Lublin, Poland.
| | - Izabela Dąbrowska
- Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, Lublin, Poland
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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Kazama T, Takahara T, Hashimoto J. Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review. Life (Basel) 2022; 12:life12040490. [PMID: 35454981 PMCID: PMC9028183 DOI: 10.3390/life12040490] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/20/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan;
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
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Cho E, Baek HJ, Szczepankiewicz F, An HJ, Jung EJ, Lee HJ, Lee J, Gho SM. Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer. Quant Imaging Med Surg 2022; 12:2002-2017. [PMID: 35284250 PMCID: PMC8899958 DOI: 10.21037/qims-21-870] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 12/13/2021] [Indexed: 08/28/2023]
Abstract
BACKGROUND Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. METHODS We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. RESULTS The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05). CONCLUSIONS Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.
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Affiliation(s)
- Eun Cho
- Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea
| | - Hye Jin Baek
- Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea
- Department of Radiology, Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju-daero, Jinju, Republic of Korea
| | - Filip Szczepankiewicz
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Klinikgatan, Sweden
| | - Hyo Jung An
- Department of Pathology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea
| | - Eun Jung Jung
- Department of Surgery, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-gu, Busan, Republic of Korea
| | | | - Sung-Min Gho
- MR Clinical Solutions & Research Collaborations, GE Healthcare, Seoul, Republic of Korea
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Liu J, Li Q, Tang L, Huang Z, Lin Q. Correlations of Mean and Mimimum Apparent Diffusion Coefficient Values With the Clinicopathological Features in Rectal Cancer. Acad Radiol 2021; 28 Suppl 1:S105-S111. [PMID: 33162315 DOI: 10.1016/j.acra.2020.10.018] [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: 09/01/2020] [Revised: 10/08/2020] [Accepted: 10/12/2020] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVE The study aimed to investigate the possible correlation between mean (MeanADC) and minimum (MinADC) apparent diffusion coefficient values with the clinicopathological features and evaluate the diagnostic potential of MinADC values and MeanADC values in predicting the behavior of rectal cancer. MATERIALS AND METHODS In total, 148 pathologically verified lesions that were subjected to conventional MR imaging and diffusion weighted imaging prior to operation were included. The MeanADC values and MinADC values were calculated and their correlation with clinicopathological characteristics were investigated. RESULTS Both MeanADC values and MinADC values correlated with T classification (MeanADC: t = 2.841, p = 0.005; MinADC: t = 2.356, p = 0.020), N classification (MeanADC: t = 3.468, p = 0.001; MinADC: t = 3.072, p = 0.003), tumor histological grade (MeanADC: F = 8.175, p = 0.000; MinADC: F = 22.038, p = 0.000), perineural invasion (MeanADC: t = 2.547, p = 0.012; MinADC: t = 3.081, p = 0.002), and extramural venous invasion (MeanADC: t = 2.157, p = 0.033; MinADC: t = 2.635, p = 0.009) in rectal cancer, but no significant correlation with gender, age, and tumor location (p > 0.05). The MinADC values showed a higher diagnostic efficacy in discriminating the well or poor differentiation of rectal cancer compared to MeanADC values, with a threshold value of ≥0.929 × 10-3 mm2/s (sensitivity, 80%; specificity, 88.1%) or ≤0.752 × 10-3 mm2/s (sensitivity, 94.1%; specificity, 74%). CONCLUSION Both MeanADC values and MinADC values might be used as a quantitative parameter to evaluate the aggressiveness of rectal cancer. The MinADC values could be as the better predictor in identifying tumor differentiation compared to the MeanADC values.
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Human Epidermal Growth Factor Receptor Type 2-Positive Breast Cancer: Association of MRI and Clinicopathologic Features With Tumor-Infiltrating Lymphocytes. AJR Am J Roentgenol 2021; 218:258-269. [PMID: 34431365 DOI: 10.2214/ajr.21.26400] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Tumor-infiltrating lymphocytes (TILs) are associated with therapeutic outcomes and prognosis in patients with human epidermal growth factor receptor type 2-positive (HER2+) breast cancer. Identification of TIL levels is clinically relevant. Objective: To explore associations of clinicopathologic and MRI features with TIL levels in patients with HER2+ breast cancer. Methods: A total of 212 consecutive women (mean age, 54 years) diagnosed with HER2+ breast cancer between January 2017 and December 2019 were included in this retrospective study. Patients were divided into low (<10%) and high (≥10%) TIL groups. Three breast radiologists independently reviewed images; interreader agreement was assessed, and the first readers' findings were used for further analysis. Associations of clinicopathologic and MRI features with TIL levels were evaluated using multivariable logistic regression analysis. Subanalysis of TIL levels by hormone receptor (HR) status was also performed. Results: A total of 115 (54.2%) patients had low, and 97 (45.8%) had high, TIL levels. High TIL level was associated (all p<.05) with histologic grade 3 (odds ratio [OR]=3.98; frequency of 78.4% vs 52.2% in high vs low TIL groups, respectively), high tumor cellularity (OR=4.59; median cellularity of 60% vs 50%), lower frequency of associated ductal carcinoma in situ (OR=0.16; frequency of 86.6% vs 94.8%), and higher frequency of peritumoral edema on T2-weighted images (OR=2.83; 71.1% vs 50.4%). In subgroup analysis by HR status, histologic grade 3 (OR=5.03, p=.002) was a significant independent predictor of high TIL in the HR+/HER2+ group, while high tumor cellularity (OR=9.06, p=.002), peritumoral edema (OR=5.23, p=.03), and low ADC (OR=11.69, p=.047) were independent predictors of high TIL in the HR-/HER2+ group. Interreader agreement for peritumoral edema was moderate among the three radiologists (к, range 0.432-0.539). Conclusion: Peritumoral edema on MRI and histopathologic feature of tumor aggressiveness help predict high TIL levels in patients with HER2+ breast cancer. Clinical Impact: Pretreatment MRI features may serve as a useful tool for assessing TIL levels in patients with HER2+ breast cancer, helping to classify patients with variable clinical outcomes related to immune activity and to guide selection among neoadjuvant chemotherapy (NAC) or HER2-targeted therapy or immunotherapy.
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Tang WJ, Jin Z, Zhang YL, Liang YS, Cheng ZX, Chen LX, Liang YY, Wei XH, Kong QC, Guo Y, Jiang XQ. Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer. Front Oncol 2021; 10:611571. [PMID: 33489920 PMCID: PMC7820903 DOI: 10.3389/fonc.2020.611571] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/19/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose To assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer. Methods In total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests. Results A statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029). Conclusions Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.
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Affiliation(s)
- Wen-Jie Tang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zhe Jin
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yan-Ling Zhang
- Department of Ultrasound, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yun-Shi Liang
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zi-Xuan Cheng
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lei-Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Ying-Ying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin-Hua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Qing-Cong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin-Qing Jiang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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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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Abstract
Non-invasive magnetic resonance imaging (MRI) techniques are increasingly applied in the clinic with a fast growing body of evidence regarding its value for clinical decision making. In contrast to biochemical or histological markers, the key advantages of imaging biomarkers are the non-invasive nature and the spatial and temporal resolution of these approaches. The following chapter focuses on clinical applications of novel MR biomarkers in humans with a strong focus on oncologic diseases. These include both clinically established biomarkers (part 1-4) and novel MRI techniques that recently demonstrated high potential for clinical utility (part 5-7).
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Affiliation(s)
- Daniel Paech
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
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Zhao Q, Xie T, Fu C, Chen L, Bai Q, Grimm R, Peng W, Wang S. Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient. Eur J Radiol 2019; 123:108782. [PMID: 31864142 DOI: 10.1016/j.ejrad.2019.108782] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 02/08/2023]
Abstract
PURPOSE The aim of this study was to investigate whether whole-lesion histogram and texture analysis using apparent diffusion coefficient can discriminate between idiopathic granulomatous mastitis (IGM) and invasive breast carcinoma (IBC), both of which appeared as non-mass enhancement lesions without rim-enhanced masses. METHOD This retrospective study included 58 pathology-proven female patients at two independent study sites (27 IGM patients and 31 IBC patients). Diffusion-weighted imaging (3b values, 50, 400 or 500, and 800 s/mm2) was performed using 1.5 T or 3 T MR scanners from the same vendor. Whole-lesions were segmented and 11 features were extracted. Univariate analysis and multivariate logistic regression analysis were performed to identify significant variables for differentiating IGM from IBC. Receiver operating characteristic curve was assessed. The interobserver reliability between two observers for the histogram and texture measurement was also reported. RESULTS The 5th percentile, difference entropy and entropy of apparent diffusion coefficient showed significant differences between the two groups. An area under the curve of 0.778 (95 % CI: 0.648, 0.908), accuracy of 79.3 %, and sensitivity of 87.1 % was achieved using these three significant features. No significant feature was found with the multivariate analysis. For the interobserver reliability, all apparent diffusion coefficient parameters except skewness and kurtosis indicated good or excellent agreement, while these two features showed moderate agreement. CONCLUSIONS Whole-lesion histogram and texture analysis using apparent diffusion coefficient provide a non-invasive analytical approach to the differentiation between IGM and IBC, both presenting with non-mass enhancement without rim-enhanced masses.
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Affiliation(s)
- Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance, Shenzhen, China
| | - Ling Chen
- Department of Pathology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qianming Bai
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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13
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Usefulness of imaging findings in predicting tumor-infiltrating lymphocytes in patients with breast cancer. Eur Radiol 2019; 30:2049-2057. [PMID: 31822972 DOI: 10.1007/s00330-019-06516-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 09/26/2019] [Accepted: 10/16/2019] [Indexed: 12/18/2022]
Abstract
OBJECTIVES Tumor-infiltrating lymphocytes (TILs) have been determined as a new prognostic indicator of immunotherapy response in breast cancer (BC). The aim of this study is to investigate the effectiveness of imaging features in predicting the TIL levels in invasive BC patients. METHODS A total of 158 patients with invasive BC were included in our study. All lesions were evaluated based on the BIRADS lexicon. US was performed for all the patients and 89 of them underwent MRI. The histologic stromal TIL (sTIL) levels were assessed and associations between the sTIL levels and imaging features were evaluated. RESULTS Tumors with high sTIL levels had more circumscribed margins, round shape, heterogeneous echogenicity, and larger size on ultrasonography (p < 0.005). There was a statistically significant positive correlation between the sTIL levels and ADC value (p < 0.001). Tumors with high sTIL levels had significantly more homogeneous enhancement than the tumors with low sTIL levels (p = 0.001). Logistic regression analysis showed that the ADC was the most statistically significant parameter in predicting the sTIL levels (the odds ratio was 90.952; p = 0.002). The optimal cutoff value for ADC in predicting low and high sTIL levels was found to be 0.87 × 10-3 mm2 s-1 (AUC = 0.726, 73% specificity, and 60% sensitivity). CONCLUSIONS Imaging findings, especially the ADC, may play an important role as an adjunct tool in cases of uncertain situations and may improve the accuracy of biopsy results. The prediction of sTIL levels using imaging findings may give an opportunity to predict prognosis. KEY POINTS • Preoperative assessment of TILs is an important biomarker of prognosis and treatment efficacy. • ADC value can be a useful tool in distinguishing high and low sTIL levels as a non-invasive method. • The prediction of sTIL levels using imaging findings may give an opportunity to predict prognosis and an optimal treatment for the BC patients.
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Fan M, Yuan W, Zhao W, Xu M, Wang S, Gao X, Li L. Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics. IEEE J Biomed Health Inform 2019; 24:1632-1642. [PMID: 31794406 DOI: 10.1109/jbhi.2019.2956351] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic analysis. METHODS We jointly predicted Ki-67 and tumor grade with a multitask learning framework by separately utilizing radiomics from tumor MRI series. Additionally, we showed how multitask learning models (MTLs) could be extended to combined radiomics from the MRI series for a better prediction based on the assumption that features from different sources of images share common patterns while providing complementary information. Tumor radiomic analysis was performed with morphological, statistical and textural features extracted on the DWI and dynamic contrast-enhanced MRI (DCE-MRI) series of the precontrast and subtraction images, respectively. RESULTS Joint prediction of Ki-67 status and tumor grade on MR images using the MTL achieved performance improvements over that of single-task-based predictive models. Similarly, for the prediction tasks of Ki-67 and tumor grade, the MTL for combined precontrast and apparent diffusion coefficient (ADC) images achieved AUCs of 0.811 and 0.816, which were significantly better than that of the single-task- based model with p values of 0.005 and 0.017, respectively. CONCLUSION Mapping MRI radiomics to two related clinical indicators improves prediction performance for both Ki-67 expression level and tumor grade. SIGNIFICANCE Joint prediction of indicators by multitask learning that combines correlations of MRI radiomics is important for optimal tumor therapy and treatment because clinical decisions are made by integrating multiple clinical indicators.
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15
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Questionable correlation of the apparent diffusion coefficient with the histological grade and microvascular invasion in small hepatocellular carcinoma. Clin Radiol 2019; 74:406.e19-406.e27. [PMID: 30826002 DOI: 10.1016/j.crad.2019.01.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 01/24/2019] [Indexed: 12/13/2022]
Abstract
AIM To evaluate the correlation between the apparent diffusion coefficient (ADC) and various histopathological parameters in small hepatocellular carcinomas (HCCs). MATERIALS AND METHODS In 143 surgically resected small HCCs, the mean and minimum ADC values, tumour-to-liver ADC ratio, and normalised ADC (ADC of the HCC/ADC of the spleen) were correlated to the tumour grade, microvascular invasion (MVI), cellularity, fatty change, degree of fibrosis, and lymphocytic infiltration using linear regression analysis, the Wilcoxon rank sum test, or Spearman's rank correlation. RESULTS No significant correlation was found between the ADC parameters and tumour grade. In the univariate analysis, the ADC ratio of the tumour was significantly correlated with MVI as well as the degree of fibrosis and lymphocyte infiltration of the HCC (p=0.017, 0.042, and 0.002, respectively). The ADC of the tumour was significantly correlated with the degree of lymphocyte infiltration of the HCC (p=0.049). In the multivariate analysis, the ADC ratio of the tumour was an independent parameter for MVI and the degree of lymphocyte infiltration of the HCC (p=0.034 and <0.001, respectively), and the ADC of the tumour was an independent parameter for the degree of lymphocyte infiltration of the HCC (p=0.009). There was no significant correlation between the other ADCs and pathological tumour parameters. CONCLUSION The tumour grade of small HCCs was not correlated with ADC parameters. The tumour-to-liver ADC ratio was a significant independent parameter for the degree of lymphocyte infiltration and MVI of small HCCs.
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16
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Correlation between apparent diffusion coefficient of magnetic resonance imaging and tumor-infiltrating lymphocytes in breast cancer. Radiol Med 2019; 124:581-587. [DOI: 10.1007/s11547-019-01008-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/11/2019] [Indexed: 12/11/2022]
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Nerad E, Delli Pizzi A, Lambregts DMJ, Maas M, Wadhwani S, Bakers FCH, van den Bosch HCM, Beets-Tan RGH, Lahaye MJ. The Apparent Diffusion Coefficient (ADC) is a useful biomarker in predicting metastatic colon cancer using the ADC-value of the primary tumor. PLoS One 2019; 14:e0211830. [PMID: 30721268 PMCID: PMC6363286 DOI: 10.1371/journal.pone.0211830] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 01/21/2019] [Indexed: 12/13/2022] Open
Abstract
Purpose To investigate the role of the apparent diffusion coefficient (ADC) as a potential imaging biomarker to predict metastasis (lymph node metastasis and distant metastasis) in colon cancer based on the ADC-value of the primary tumor. Methods Thirty patients (21M, 9F) were included retrospectively. All patients received a 1.5T MRI of the colon including T2 and DWI sequences. ADC maps were calculated for each patient. An expert reader manually delineated all colon tumors to measure mean ADC and histogram metrics (mean, min, max, median, standard deviation (SD), skewness, kurtosis, 5th-95th percentiles) were calculated. Advanced colon cancer was defined as lymph node mestastasis (N+) or distant metastasis (M+). The student Mann Whitney U-test was used to assess the differences between the ADC means of early and advanced colon cancer. To compare the accuracy of lymph node metastasis (N+) prediction based on morpholigical criteria versus ADC-value of the primary tumor, two blinded readers, determined the lymph node metastasis (N0 vs N+) based on morphological criteria. The sensitivity and specificity in predicting lymph node metastasis was calculated for both readers and for the ADC-value of the primary tumor, with histopathology results as the gold standard. Results There was a significant difference between the mean ADC-value of advanced versus early tumors (p = 0.002). The optimal cut off value was 1179 * 10−3 mm2/s with an area under the curve (AUC) of 0.83 and a sensitivity and specificity of 81% and 86% respectively to predict advanced tumors. Histogram analyses did not add any significant additional value. The sensitivity and specificity for the prediction of lymph node metastasis based on morphological criteria were 40% and 63% for reader 1 and 30% and 88% for reader 2 respectively. The primary tumor ADC-value using 1.179 * 10−3 mm2/s as threshold had a 100% sensitivity and specificity in predicting lymph node metastasis. Conclusion The ADC-value of the primary tumor has the potential to predict advanced colon cancer, defined as lymph node metastasis or distant metastasis, with lower ADC values significantly associated with advanced tumors. Furthermore the ADC-value of the primary tumor increases the prediction accuracy of lymph node metastasis compared with morphological criteria.
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Affiliation(s)
- Elias Nerad
- University of Maastricht and GROW School of Oncology and Developmental Biology, Maastricht, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiology, Addenbrookes Hospital Cambridge University Hospitals NHS trust, Cambridge, United Kingdom
- * E-mail:
| | - Andrea Delli Pizzi
- Institute for Advanced Biomedical Technology (ITAB), Gabriele d'Annunzio University, Chieti, Italy
| | | | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sharan Wadhwani
- Department of radiology, Queen Elizabeth Hospital, University Birmingham Hospitals NHS trust, Birmingham, United Kingdom
| | - Frans C. H. Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - Regina G. H. Beets-Tan
- University of Maastricht and GROW School of Oncology and Developmental Biology, Maastricht, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Max J. Lahaye
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Chen Y, Wu B, Liu H, Wang D, Gu Y. Feasibility study of dual parametric 2D histogram analysis of breast lesions with dynamic contrast-enhanced and diffusion-weighted MRI. J Transl Med 2018; 16:325. [PMID: 30470241 PMCID: PMC6260880 DOI: 10.1186/s12967-018-1698-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 11/16/2018] [Indexed: 01/01/2023] Open
Abstract
Background This study aimed to investigate the diagnostic value of a dual-parametric 2D histogram classification method for breast lesions. Methods This study included 116 patients with 72 malignant and 44 benign breast lesions who underwent CAIPIRINHA-Dixon-TWIST-VIBE dynamic contrast-enhanced (CDT-VIBE DCE) and readout-segmented diffusion-weighted magnetic resonance examination. The volume of interest (VOI), which encompassed the entire lesion, was segmented from the last phase of DCE images. For each VOI, a 1D histogram analysis (mean, median, 10th percentile, 90th percentile, kurtosis and skewness) was performed on apparent diffusion coefficient (ADC) and volume transfer constant (Ktrans) maps; a 2D histogram image (Ktrans-ADC) was generated from the pixelwise aligned maps, and its kurtosis and skewness were calculated. Each parameter was correlated with pathological results using the Mann–Whitney test and receiver operating characteristic curve analysis. Results For the Ktrans histogram, the area under the curve (AUC) of the mean, median, 90th percentile and kurtosis had statistically diagnostic values (mean: 0.760; median: 0.661; 90th percentile: 0.781; and kurtosis: 0.620). For the ADC histogram, the AUC of the mean, median, 10th percentile, skewness and kurtosis had statistically diagnostic values (mean: 0.661; median: 0.677; 10th percentile: 0.656; skewness: 0.664; and kurtosis: 0.620). For the 2D Ktrans-ADC histogram, the skewness and kurtosis had statistically higher diagnostic values (skewness: 0.831, kurtosis: 0.828) than those of the 1D histogram (all P < 0.05). Conclusions The dual-parametric 2D histogram analysis revealed better diagnostic accuracy for breast lesions than single parametric histogram analysis of either Ktrans or ADC maps.
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Affiliation(s)
- Yanqiong Chen
- Fudan University Shanghai Cancer Center, No. 270, Dong'an Rd, Shanghai, 200032, China
| | - Bin Wu
- Fudan University Shanghai Cancer Center, No. 270, Dong'an Rd, Shanghai, 200032, China
| | - Hui Liu
- Imaging Technology (Shanghai), Shanghai, China
| | - Dan Wang
- Fudan University Shanghai Cancer Center, No. 270, Dong'an Rd, Shanghai, 200032, China
| | - Yajia Gu
- Fudan University Shanghai Cancer Center, No. 270, Dong'an Rd, Shanghai, 200032, China.
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Deike-Hofmann K, Koenig F, Paech D, Dreher C, Delorme S, Schlemmer HP, Bickelhaupt S. Abbreviated MRI Protocols in Breast Cancer Diagnostics. J Magn Reson Imaging 2018; 49:647-658. [PMID: 30328180 DOI: 10.1002/jmri.26525] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 09/11/2018] [Accepted: 09/12/2018] [Indexed: 12/12/2022] Open
Abstract
Oncologic imaging focused on the detection of breast cancer is of increasing importance, with over 1.7 million new cases detected each year worldwide. MRI of the breast has been described to be one of the most sensitive imaging modalities in breast cancer detection; however, clinical use is limited due to high costs. In the past, the objective and clinical routine of oncologic imaging was to provide one extended imaging protocol covering all potential needs and clinical implications regardless of the specific clinical indication or question. Future protocols might be more focused according to a "keep it short and simple" approach, with a reduction of patient magnet time and a limited number of images to review. Rather than replacing conventional full-diagnostic breast MRI protocols, these approaches aim at introducing a new thinking in oncologic imaging using a diversification of available imaging approaches targeted to the dedicated clinical needs of the individual patient. Here we review current approaches on using abbreviated protocols that aim to increase the clinical availability and use of breast MRI for improved early detection of breast cancer. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:647-658.
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Affiliation(s)
| | - Franziska Koenig
- German Cancer Research Center (dkfz), Department of Radiology, Heidelberg, Germany
| | - Daniel Paech
- German Cancer Research Center (dkfz), Department of Radiology, Heidelberg, Germany
| | - Constantin Dreher
- German Cancer Research Center (dkfz), Department of Radiology, Heidelberg, Germany
| | - Stefan Delorme
- German Cancer Research Center (dkfz), Department of Radiology, Heidelberg, Germany
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Igarashi T, Furube H, Ashida H, Ojiri H. Breast MRI for prediction of lymphovascular invasion in breast cancer patients with clinically negative axillary lymph nodes. Eur J Radiol 2018; 107:111-118. [DOI: 10.1016/j.ejrad.2018.08.024] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 08/01/2018] [Accepted: 08/26/2018] [Indexed: 12/22/2022]
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Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging. Eur Radiol 2018; 29:1425-1434. [PMID: 30116958 DOI: 10.1007/s00330-018-5667-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 06/19/2018] [Accepted: 07/13/2018] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging. METHODS One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated. RESULTS Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative. CONCLUSIONS Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination. KEY POINTS • ADC metrics are associated with molecular marker status in IDC. • ADC 1500 improves differentiation of histologic phenotypes compared with ADC 800 . • ADC metrics add value to morphologic features in IDC phenotyping.
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Abstract
Magnetic resonance imaging (MRI) of the breast represents one of the most sensitive imaging modalities in breast cancer detection. Diffusion-weighted imaging (DWI) is a sequence variation introduced as a complementary MRI technique that relies on mapping the diffusion process of water molecules thereby providing additional information about the underlying tissue. Since water diffusion is more restricted in most malignant tumors than in benign ones owing to the higher cellularity of the rapidly proliferating neoplasia, DWI has the potential to contribute to the identification and characterization of suspicious breast lesions. Thus, DWI might increase the diagnostic accuracy of breast MRI and its clinical value. Future applications including optimized DWI sequences, technical developments in MR devices, and the application of radiomics/artificial intelligence algorithms may expand the potential of DWI in breast imaging beyond its current supplementary role.
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Zhao S, Guo W, Tan R, Chen P, Li Z, Sun F, Shao G. Correlation between minimum apparent diffusion coefficient values and the histological grade of breast invasive ductal carcinoma. Oncol Lett 2018; 15:8134-8140. [PMID: 29849809 DOI: 10.3892/ol.2018.8343] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 12/06/2018] [Indexed: 01/15/2023] Open
Abstract
The present study aimed to investigate the correlation between the minimum apparent diffusion coefficient (ADCmin) value and the histological grade of breast invasive ductal carcinoma (IDC). In total, 129 pathologically verified lesions that were subjected to dynamic breast magnetic resonance imaging and diffusion weighted imaging prior to biopsy were included. The ADCmin value was calculated and its correlation with the tumor histological grade was investigated. Tumors of lower grades demonstrated significantly higher ADCmin values as compared with tumors of higher grades (F=33.49; P<0.01). The mean ADCmin values for IDC of grades I, II and III were (1.14±0.11)×10-3, (0.99±0.12)×10-3 and (0.86±0.13)×10-3 mm2/sec, respectively. Statistically significant differences were detected in the mean ADCmin value between tumors of grades II and III (P<0.01), as well as between tumors of grades I and II (P<0.01). In addition, the mean ADCmin values for the less aggressive (grades I and II) and more aggressive (grade III) groups were (1.01±0.13)×10-3 and (0.86±0.13)×10-3 mm2/sec, respectively (t=5.76, P<0.01). In conclusion, these data indicated that the ADCmin value was correlated with the IDC histological grade, and lower ADCmin values were associated with a higher histological grade and more aggressiveness. Thus, the ADCmin value may be considered as a promising prognostic parameter in identifying tumor aggressiveness.
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Affiliation(s)
- Suhong Zhao
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Weihua Guo
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Ru Tan
- Department of Radiology, Provincial Hospital of Shandong University, Jinan, Shandong 250021, P.R. China
| | - Peipei Chen
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Zhaohua Li
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Fengguo Sun
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
| | - Guangrui Shao
- Department of Radiology, The Second Hospital of Shandong University, Jinan, Shandong 250033, P.R. China
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Surov A, Meyer HJ, Winter K, Richter C, Hoehn AK. Histogram analysis parameters of apparent diffusion coefficient reflect tumor cellularity and proliferation activity in head and neck squamous cell carcinoma. Oncotarget 2018; 9:23599-23607. [PMID: 29805759 PMCID: PMC5955087 DOI: 10.18632/oncotarget.25284] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/06/2018] [Indexed: 11/26/2022] Open
Abstract
Our purpose was to analyze associations between apparent diffusion coefficient (ADC) histogram analysis parameters and histopathologicalfeatures in head and neck squamous cell carcinoma (HNSCC). The study involved 32 patients with primary HNSCC. For every tumor, the following histogram analysis parameters were calculated: ADCmean, ADCmax, ADCmin, ADCmedian, ADCmode, P10, P25, P75, P90, kurtosis, skewness, and entropy. Furthermore, proliferation index KI 67, cell count, total and average nucleic areas were estimated. Spearman's correlation coefficient (p) was used to analyze associations between investigated parameters. In overall sample, all ADC values showed moderate inverse correlations with KI 67. All ADC values except ADCmax correlated inversely with tumor cellularity. Slightly correlations were identified between total/average nucleic area and ADCmean, ADCmin, ADCmedian, and P25. In G1/2 tumors, only ADCmode correlated well with Ki67. No statistically significant correlations between ADC parameters and cellularity were found. In G3 tumors, Ki 67 correlated with all ADC parameters except ADCmode. Cell count correlated well with all ADC parameters except ADCmax. Total nucleic area correlated inversely with ADCmean, ADCmin, ADCmedian, P25, and P90. ADC histogram parameters reflect proliferation potential and cellularity in HNSCC. The associations between histopathology and imaging depend on tumor grading.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University Hospital of Leipzig, Leipzig 04103, Germany
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University Hospital of Leipzig, Leipzig 04103, Germany
| | - Karsten Winter
- Institute of Anatomy, University Hospital of Leipzig, Leipzig 04103, Germany
| | - Cindy Richter
- Institute of Anatomy, University Hospital of Leipzig, Leipzig 04103, Germany
| | - Anna-Kathrin Hoehn
- Department of Pathology, University Hospital of Leipzig, Leipzig 04103, Germany
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Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive of the available imaging modalities to characterize breast cancer. Breast MRI has gained clinical acceptance for screening high-risk patients, but its role in the preoperative imaging of breast cancer patients remains controversial. This review focuses on the current indications for staging breast MRI, the evidence for and against the role of breast MRI in the preoperative staging workup, and the evaluation of treatment response of breast cancer patients.
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Shen L, Zhou G, Tong T, Tang F, Lin Y, Zhou J, Wang Y, Zong G, Zhang L. ADC at 3.0 T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast. Clin Imaging 2018; 52:16-22. [PMID: 29501957 DOI: 10.1016/j.clinimag.2018.02.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 01/24/2018] [Accepted: 02/12/2018] [Indexed: 11/17/2022]
Abstract
PURPOSE To investigate the role of apparent diffusion coefficient (ADC) as an imaging biomarker for invasive ductal carcinoma (IDC) in the breast. METHODS Seventy-one patients undergoing 3.0 Tesla DWI were retrospectively enrolled. Correlations between the ADC values and prognostic factors were evaluated. RESULTS Multivariate regression analyses showed that Ki67 expression and molecular subtype were independently associated with the ADC. Discriminant analysis excluded the ADC as a good biomarker for subtype, but the mean ADC significantly distinguished Ki67-positive (low ADC) from Ki67-negative (high ADC) lesions, as observed in the in ROC curves, with a diagnostic sensitivity of 1.00 and a cut-off value of 0.97 × 10-3 mm2/s. CONCLUSION The ADC may be helpful for predicting Ki67 expression in IDC preoperatively.
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Affiliation(s)
- Lu Shen
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Guoxing Zhou
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Tong Tong
- Department of Radiology, Shanghai Cancer Center, School of Medicine, Fudan University, Shanghai, 200032, China
| | - Fei Tang
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Yi Lin
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Jie Zhou
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Yibin Wang
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Genlin Zong
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Lei Zhang
- Department of Radiology, East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
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Fan M, He T, Zhang P, Cheng H, Zhang J, Gao X, Li L. Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer. NMR IN BIOMEDICINE 2018; 31:e3869. [PMID: 29244222 DOI: 10.1002/nbm.3869] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/28/2017] [Accepted: 10/30/2017] [Indexed: 06/07/2023]
Abstract
Breast cancer heterogeneity is the main obstacle preventing the identification of patients with breast cancer with poor prognoses and treatment responses; however, such heterogeneity has not been well characterized. The purpose of this retrospective study was to reveal heterogeneous patterns in the apparent diffusion coefficient (ADC) signals in tumours and the surrounding stroma to predict molecular subtypes of breast cancer. A dataset of 126 patients with breast cancer, who underwent preoperative diffusion-weighted imaging (DWI) on a 3.0-T image system, was collected. Breast images were segmented into regions comprising the tumour and surrounding stromal shells in which features that reflect heterogeneous ADC signal distribution were extracted. For each region, imaging features were computed, including the mean, minimum, variance, interquartile range (IQR), range, skewness, kurtosis and entropy of ADC values. Univariate and stepwise multivariate logistic regression modelling was performed to identify the magnetic resonance imaging features that optimally discriminate luminal A, luminal B, human epidermal growth factor 2 (HER2)-enriched and basal-like molecular subtypes. The performance of the predictive models was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate logistic regression analysis showed that the skewness in the tumour boundary achieved an AUC of 0.718 for discrimination between luminal A and non-luminal A tumours, whereas the IQR of the ADC value in the tumour boundary had an AUC of 0.703 for classification of the HER2-enriched subtype. Imaging features in the tumour boundary and the proximal peritumoral stroma corresponded to a higher overall prediction performance than those in other regions. A multivariate logistic regression model combining features in all the regions achieved an overall AUC of 0.800 for the classification of the four tumour subtypes. These findings suggest that features in the tumour boundary and stroma around the tumour may be further assessed as potential predictors of molecular subtypes of breast cancer.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Ting He
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Peng Zhang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Hu Cheng
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Juan Zhang
- Zhejiang Cancer Hospital, Zhejiang, Hangzhou, China
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Saudi Arabia
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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Abstract
OBJECTIVE The goals of this review are to provide background information on the definitions and applications of the general term "biomarker" and to highlight the specific roles of breast imaging biomarkers in research and clinical breast cancer care. A search was conducted of the main electronic biomedical databases (PubMed, Cochrane, Embase, MEDLINE [Ovid], Scopus, and Web of Science). The search was focused on review literature in general radiology and biomedical sciences and on reviews and primary research articles on biomarkers in breast imaging over the 15 years ending in June 2017. The keywords included "biomarker," "trial endpoints," "breast imaging," "breast cancer," "radiomics," and "precision medicine" in the titles and abstracts of the papers. CONCLUSION Clinical breast care and breast cancer-related research rely on imaging biomarkers for decision support. In the era of precision medicine and big data, the practice of radiology is likely to change. A closer integration of breast imaging with related biomedical fields and the creation of large integrated and shareable databases of clinical, molecular, and imaging biomarkers should allow the field to continue guiding breast cancer care and research.
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Fowler AM, Mankoff DA, Joe BN. Imaging Neoadjuvant Therapy Response in Breast Cancer. Radiology 2017; 285:358-375. [DOI: 10.1148/radiol.2017170180] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Amy M. Fowler
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252 (A.M.F.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (D.A.M.); and Department of Radiology and Biomedical Imaging, University of California–San Francisco School of Medicine, San Francisco, Calif (B.N.J.)
| | - David A. Mankoff
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252 (A.M.F.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (D.A.M.); and Department of Radiology and Biomedical Imaging, University of California–San Francisco School of Medicine, San Francisco, Calif (B.N.J.)
| | - Bonnie N. Joe
- From the Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792-3252 (A.M.F.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (D.A.M.); and Department of Radiology and Biomedical Imaging, University of California–San Francisco School of Medicine, San Francisco, Calif (B.N.J.)
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Surov A, Meyer HJ, Wienke A. Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADC mean. Oncotarget 2017; 8:75434-75444. [PMID: 29088879 PMCID: PMC5650434 DOI: 10.18632/oncotarget.20406] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 08/15/2017] [Indexed: 02/07/2023] Open
Abstract
Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues. This diffusion can be quantified by apparent diffusion coefficient (ADC). Some reports indicated that ADC can reflect tumor proliferation potential. The purpose of this meta-analysis was to provide evident data regarding associations between ADC and KI 67 in different tumors. Studies investigating the relationship between ADC and KI 67 in different tumors were identified. MEDLINE library was screened for associations between ADC and KI 67 in different tumors up to April 2017. Overall, 42 studies with 2026 patients were identified. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients. Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients. The pooled correlation coefficient between ADCmean and KI 67 for all included tumors was ρ = -0.44. Furthermore, correlation coefficient for every tumor entity was calculated. The calculated correlation coefficients were as follows: ovarian cancer: ρ = -0.62, urothelial carcinomas: ρ = -0.56, cerebral lymphoma: ρ = -0.55, neuroendocrine tumors: ρ = -0.52, glioma: ρ = -0.51, lung cancer: ρ = -0.50, prostatic cancer: ρ = -0.43, rectal cancer: ρ = -0.42, pituitary adenoma:ρ = -0.44, meningioma, ρ = -0.43, hepatocellular carcinoma: ρ = -0.37, breast cancer: ρ = -0.22.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin Luther University of Halle-Wittenberg, Halle, Germany
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31
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Xu L, Wang RP, Zhang S, Chen ZG. Comments on 'Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness'. NMR IN BIOMEDICINE 2017; 30:e3746. [PMID: 28544012 DOI: 10.1002/nbm.3746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Revised: 04/10/2017] [Accepted: 04/10/2017] [Indexed: 06/07/2023]
Affiliation(s)
- Li Xu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong Province, China
| | - Rong-Pin Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou Province, China
| | - SiWei Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong Province, China
| | - Zhi-Guang Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong Province, China
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Heterogeneity of Diffusion-Weighted Imaging in Tumours and the Surrounding Stroma for Prediction of Ki-67 Proliferation Status in Breast Cancer. Sci Rep 2017; 7:2875. [PMID: 28588280 PMCID: PMC5460128 DOI: 10.1038/s41598-017-03122-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 04/24/2017] [Indexed: 12/23/2022] Open
Abstract
Breast tissue heterogeneity is related to risk factors that lead to more aggressive tumour growth and worse prognosis, yet such heterogeneity has not been well characterized. The aim of this study is to reveal the heterogeneous signal patterns of the apparent diffusion coefficient (ADC) of a tumour and its surrounding stromal tissue and to predict the Ki-67 proliferation status in oestrogen receptor (ER)-positive breast cancer patients. A dataset of 82 patients who underwent diffusion-weighted imaging (DWI) examination was collected. The ADC map was segmented into regions comprising the tumour and the surrounding stromal shells. To reflect correlations between each region in terms of its mean ADC value, a functional graph was constructed consisting of nodes as regions and edges as interactions between two nodes. Analysis of the graph revealed a higher average degree in samples over-expressing Ki-67 than in samples with low Ki-67 expression. In the low-Ki-67 group, most of the identified edges represented correlations between adjacent regions, whereas additional edges representing correlations between non-adjacent regions were found in the high-Ki-67 group. The ADC signal in various breast stromal regions surrounding the tumour showed a discriminative pattern and would be valuable for estimating the Ki-67 proliferation status by DWI.
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Surov A, Meyer HJ, Wienke A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget 2017; 8:59492-59499. [PMID: 28938652 PMCID: PMC5601748 DOI: 10.18632/oncotarget.17752] [Citation(s) in RCA: 207] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 04/27/2017] [Indexed: 01/29/2023] Open
Abstract
The purpose of this meta-analysis was to provide clinical evidence regarding relationship between ADC and cellularity in different tumors based on large patient data. Medline library was screened for associations between ADC and cell count in different tumors up to September 2016. Only publications in English were extracted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research. Overall, 39 publications with 1530 patients were included into the analysis. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients. The pooled correlation coefficient for all studies was ρ = -0.56 (95 % CI = [−0.62; −0.50]),. Correlation coefficients ranged from ρ =−0.25 (95 % CI = [−0.63; 0.12]) in lymphoma to ρ=−0.66 (95 % CI = [−0.85; −0.47]) in glioma. Other coefficients were as follows: ovarian cancer, ρ = −0.64 (95% CI = [−0.76; −0.52]); lung cancer, ρ = −0.63 (95 % CI = [−0.78; −0.48]); uterine cervical cancer, ρ = −0.57 (95 % CI = [−0.80; −0.34]); prostatic cancer, ρ = −0.56 (95 % CI = [−0.69; −0.42]); renal cell carcinoma, ρ = −0.53 (95 % CI = [−0.93; −0.13]); head and neck squamous cell carcinoma, ρ = −0.53 (95 % CI = [-0.74; −0.32]); breast cancer, ρ = −0.48 (95 % CI = [−0.74; −0.23]); and meningioma, ρ = -0.45 (95 % CI = [−0.73; −0.17]).
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
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