Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Abstracts - RGCON 2016
Case Report
Commentary
Editorial
Erratum
Letter to Editor
Letter to the Editor
Original Article
Point of Technique
Review Article
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Abstracts - RGCON 2016
Case Report
Commentary
Editorial
Erratum
Letter to Editor
Letter to the Editor
Original Article
Point of Technique
Review Article
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Abstracts - RGCON 2016
Case Report
Commentary
Editorial
Erratum
Letter to Editor
Letter to the Editor
Original Article
Point of Technique
Review Article
View/Download PDF

Translate this page into:

Original Article
2023
:9;
16
doi:
10.25259/ASJO-2022-56-(412)

Role of SUV and ADC values as a predictors of grade and molecular subtypes of breast malignancy

Department of Radiodiagnosis, Apollo Specialty Hospital, Teynampet, Chennai, India
Corresponding author: Dr. Banupriya Ramakrishnan, Department of Radiodiagnosis, Apollo Specialty Hospital, Teynampet, Chennai, India. banupriyacr11@gmail.com
Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Ramakrishnan B, Sivaramalingam G, Raghavan B, Govindaraj J, Viswanathan S, Umretiya N. Role of SUV and ADC values as a predictor of grade and molecular subtypes of breast malignancy. Asian J Oncol. 2023;9:16. doi: 10.25259/ASJO-2022-56-(412)

Abstract

Objectives

The purpose of the study is to evaluate the role of Standardized Uptake Value (SUV) and Apparent Diffusion Coefficient (ADC) values as a predictor of histologic grade and molecular subtype of breast malignancy and to evaluate the correlation of grade of malignancy with background parenchymal uptake, background parenchymal enhancement and fibroglandular tissue of the contralateral normal breast

Material and Methods

53 patients with unilateral breast cancer were included in the study. Images from Computed Tomography (CT) and Positron Emission Tomography (PET) were analyzed measuring maximum SUV and background SUV from the contralateral normal breast by placing a single Region of interest (ROI). From Diffusion-weighted magnetic resonance imaging (DWI-MRI) images ADC values were calculated with b value 0–1200 s/mm2 and single ROI placed in an area corresponding to the ROI placed to obtain maximum SUV of the mass. Type of fibroglandular tissue and background parenchymal enhancement was categorized based on Breast Imaging-Reporting and Data System (BI-RADS)–lexicon on T1 weighted and Dynamic Contrast-Enhanced (DCE) images respectively. Necrotic and hemorrhagic areas within the mass were excluded in both positron emission tomography–computed tomography (PET-CT) and Magnetic resonance imaging (MRI) while calculating SUV and ADC.

Results

A positive correlation was found between grade and Mean SUVmax with higher values in grade 3 malignancy (11.41 ± 4.76) (p-value – 0.003). Statistically significant variation in SUVmax was seen among estrogen receptor/progesterone receptor (ER/PR) status with low values in ER/PR positive tumors (p-value < 0.05). There was significant correlation between the molecular subtypes with higher SUVmax in triple-negative tumors (12.27 ± 4.22) (p-value – 0.02). Significant variation in ADC values among different molecular subtypes was seen with higher values in human epidermal growth factor receptor (HER2)-Enriched tumors (1.032 ± 0.25) and low values in luminal A subtype (0.798 ± 0.13).

Conclusion

Therefore, PET-CT and MRI can be used as a complementary imaging tool in assessing the aggressiveness and biological characteristics of tumors.

Keywords

Breast carcinoma
Tumor grades
Molecular subtypes
Receptor status
Prognostic factors
Imaging
MRI
PET-CT
SUV
DWI
ADC values
Background parenchymal uptake
Background parenchymal enhancement
Fibroglandular pattern
Fibroglandular tissue (FGT)

INTRODUCTION

Breast cancer has ranked number one among Indian females with an age-adjusted rate of 25.8 per 1,00,000 women and mortality of 12.7 per 1,00,000 women.[1] Multiple risk factors, such as age, parity, family history, BReast CAncer (BRCA) gene mutation, sedentary lifestyle, hormonal replacement therapy, and radiation exposure that are involved in the development of breast cancer have been studied.[27] Several clinico-pathological factors have been studied and correlated with the prognosis of breast cancer.

Predicting the prognosis of breast cancer is important for determining the treatment protocol. Currently histopathological grading is the most commonly used factor for assessing the aggressiveness of the lesion and is a strong predictor of prognosis[8,9] that needs invasive procedure such as incision or excision biopsy for evaluation. Nottingham Histologic Score system is used to classify the tumor as Grade 1, 2, or 3 by taking into consideration the amount of gland formation (cell “differentiation”), nuclear features (degree of “pleomorphism”), and mitotic activity (how much the tumor cells are dividing, or proliferating).

Another recently used factor for predicting prognosis is the receptor status that is also proved to be useful in determining targeted therapy. Immunohistochemical (IHC) techniques are utilized to measure the expression of receptors such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2).[1012]

Based on the receptor status molecular subtyping (luminal and non-luminal) is done.

  • -

    Luminal subtype:

    • Luminal A: A high expression of ER-related genes and low expression of HER2 and proliferation-related genes (ki67 index). ER+ PR+ HER2–, usually low grade. Most common among the luminal types and have the best prognosis

    • Luminal B: A lower expression of ER-related genes, variable expression of HER2 gene clusters, and higher expression of proliferation-related genes (ki67 index) ER+ PR+ HER2+, usually intermediate to high grade

  • -

    Non-luminal subtypes

    • HER2-Enriched: A high expression of HER2 and low expression of ER and PR, usually mid to high grade

    • Triple-negative: ER-negative, PR-negative, and HER2-negative. These tumors have worse prognosis among all subtypes[13]; with higher proliferation rates, and predominantly high-grade tumors.

Imaging plays an important role in screening, diagnosis, and staging of breast cancer. Commonly used modalities are mammography, ultrasound, positron emission tomography–computed tomography (PET-CT), and Magnetic Resonance Imaging (MRI). Few imaging features have been correlated with the risk and prognosis of breast cancer. For example, there is increased risk of breast cancer in patients with increased parenchymal density.[14,15] Poor prognosis is seen in patients with larger lesion, nodal involvement, tumor necrosis, extensive intraductal component, lymphovascular invasion, and multifocal or multicentric disease.[1621]

Functional imaging techniques such as MRI and PET are used primarily for staging, but were found to have a role to play in assessing the tumor aggressiveness.

18F-fluorodeoxyglucose (FDG) PET-CT is used for the staging, assessment of recurrence and treatment response.[22] FDG avidity reflects the cellularity of the lesion and glucose metabolism in cancer cells. It also helps in predicting the prognosis of primary breast cancer as it is associated with few histopathological and immunohistochemical prognostic factors,[23,24] such as the grade of malignancy in breast carcinoma.

MRI is known to be a highly sensitive, noninvasive technique for the detection and local staging of breast cancer. The diffusion-weighted image (DWI) in MRI is used to evaluate the microstructural characteristics of water diffusion in biological tissues.[25] As malignant mass has increased the cell proliferation, it shows restricted diffusion as a result of inhibition of water diffusion. The apparent diffusion coefficient (ADC) is a quantitative measure of the diffusion of water molecules within the tissues. Several studies have shown that the ADC value is useful for differentiating benign and malignant breast lesions.[26,27] Recently, various studies have evaluated the relationship between tumor prognostic factors and DWI or ADC values.[2832] Thus, it can help us identify tumors with high malignant potential and can provide preoperative prognostic information.

Additionally, information about background parenchymal uptake, background parenchymal enhancement, and fibroglandular tissue can serve as an important imaging biomarker in breast cancer, which has to be further evaluated.[33]

In this study, we intend to correlate these imaging findings with histological and immunohistochemical prognostic factors. By studying all these factors, imaging can have a better role to perform as a non-invasive tool in predicting the aggressiveness of the tumor and thus the prognosis.

MATERIAL AND METHODS

This is a prospective observational study including patients with biopsy-proven breast carcinoma who were referred for PET-CT. The study has been approved by the Institutional Ethics Committee (IEC) of Apollo Hospitals, Chennai; the approval reference number being - ECR/37/Inst/TN/2013/RR-16.

Inclusion criteria

  • -

    All adult patients with newly diagnosed biopsy-proven breast carcinoma who were referred for PET-CT

Exclusion criteria

  • -

    Patients with bilateral breast carcinoma

  • -

    Patients who had undergone surgery, chemotherapy, or radiotherapy

  • -

    Recurrent breast carcinoma

  • -

    Patients with contraindication for MRI

  • -

    Patients not willing to consent

Imaging technique

Every patient underwent a whole-body PET-CT imaging using a combined PET-CT scanner (SIEMENS BIOGRAPH MCT 42 slice) at least 2 weeks after the invasive biopsy. Spiral CT was acquired first in a craniocaudal direction, with 200–360 mas, 90–120 kvp. Subsequently, the PET scan was performed in a reverse longitudinal direction. Field of the scan was from vertex of skull to mid-thigh. A nonionic intravenous contrast agent (1 mL/kg body weight with saline bolus chasing) was given to improve the CT diagnostic accuracy.

CT image was used for attenuation correction and lesion localization. Displayed data includes maximum intensity projection (MIP), three plane PET, three plane CT, and PET-CT fusion images.

MRI was performed for every patient using a 1.5 Tesla Philips Achieva MRI scanner within 1–6 days after PET-CT acquisition. Following the patient’s informed consent and exclusion of contraindications, imaging was done in a prone position using a dedicated 8-channel breast coil. T1, multiphase dynamic post-contrast, and diffusion-weighted sequences were obtained.

Image analysis

The CT and PET images were analyzed by the principal investigator. Maximum SUV of the mass was calculated by placing a single ROI in an area with the highest FDG uptake within the mass (10–60 mm2). Background SUV from the contralateral normal breast was calculated by placing ROI in the fibroglandular tissue of approximate area 50 mm2.

From DWI-MRI images, ADC values were calculated with “b value” of 0–1,200 s/mm2. Single ROI was placed in an area (10–60 mm2) within the lesion corresponding to the ROI placed to obtain maximum SUV of the mass, and ADC values were measured. With T1-weighted image type of fibroglandular tissue and with DCE images background parenchymal enhancement of contralateral normal breast was categorized based on BIRADS–lexicon. Necrotic and hemorrhagic areas within the mass were excluded in both PET-CT and MRI while calculating SUV and ADC.

The histopathological report including the grade of malignancy, immunohistochemical analysis, and molecular subtypes were assessed. Molecular subtypes were classified based on receptor status ER, PR, and HER2. Imaging findings of MRI and PET-CT were compared with the histopathological findings and were documented for each patient.

RESULTS

In our prospective study, 53 patients with biopsy-proven unilateral breast cancer were included. Clinical, histopathological, and imaging characteristics of the patient are provided in Table 1. There is a significant difference in mean SUVmax values between the grades of malignancy (P = 0.003) [Figure 1], positive and negative estrogen receptor tumors [P = 0.04], positive and negative progesterone receptor tumors [P = 0.001] and among different molecular subtypes (P = 0.018) [Figure 2 and Table 2]. Higher mean SUVmax values were seen in Grade 3 tumors (11.41 ± 4.76) [Figure 1], ER negative (10.64 ± 4.37), PR negative tumors (11.64 ± 4.12), and triple-negative molecular subtype (12.27 ± 4.22) [Figure 2]. No significant difference was observed between SUV values of positive and negative HER2 receptor tumors.

Table 1: Clinical, histopathological and imaging characteristics.
Variables Number Percentage (%)
Clinical characteristics
Age
 <40 years 14/53 26.4
 >40 years 39/53 73.6
Menstrual status
 Premenopausal 20/53 37.7
 Post-menopausal 33/53 62.3
Histopathological characteristics
Grade
 Grade 1 3 6
 Grade 2 26 49
 Grade 3 24 45
Histological type
 Invasive ductal carcinoma 46 87
 Invasive lobular carcinoma 3 5
 Mucinous 2 4
 Invasive medullary carcinoma 1 2
 Metaplastic carcinoma 1 2
ER status
 Positive 31 58.5
 Negative 22 41.5
PR status
 Positive 32 60.4
 Negative 21 39.6
HER2 status
 Positive 19 35.8
 Negative 34 64.2
Molecular subtypes
 Luminal A 21 40
 Luminal B 14 26
 HER2-Enriched 5 9
 Triple negative 13 25
Imaging characteristics
Fibroglandular pattern
 Almost entirely fat 10 19
 Scattered 18 34
 Heterogenous 20 38
 Extreme 5 9
Background parenchymal enhancement
 Minimal 20 38
 Mild 18 34
 Moderate 10 19
 Marked 5 9

ER: Estrogen receptor, PR: Progesterone receptor PR, HER2: Human epidermal growth factor receptor 2

Table 2: Comparison of histopathological factors with Standardized Uptake Value and Apparent Diffusion Coefficient values.
Histopathological factor Number SUVmax P ADC (×10−3 mm2/s) P Background SUV of contralateral breast P
Grade
Grade 1 3 3.93 ± 0.53 0.003 0.79 ± 0.19 0.597 0.86 ± 0.22 0.200
Grade 2 26 7.23 ± 4.84 0.91 ± 0.19 1.28 ± 0.64
Grade 3 24 11.41 ± 4.76 0.90 ± 0.19 1.04 ± 0.43
ER status
Positive 31 7.72 ± 5.46 0.043 0.85 ± 0.16 0.025 1.13 ± 0.54 0.792
Negative 22 10.64 ± 4.37 0.97± 0.21 1.17 ± 0.56
PR status
Positive 32 7.15 ± 5.11 0.001 0.86 ± 0.16 0.100 1.14 ± 0.55 0.981
Negative 21 11.6 ± 4.12 0.95 ± 0.22 1.15 ± 0.55
HER2 status
Positive 19 7.48 ± 3.92 0.130 0.94 ± 0.15 0.229 1.07 ± 0.56 0.431
Negative 34 9.74 ± 5.68 0.88 ± 0.21 1.19 ± 0.54
Molecular subtypes
Luminal A 21 8.60 ± 6.04 0.018 0.79 ± 0.14 0.009 1.16 ± 0.50 0.652
Luminal B 14 6.12 ± 3.48 0.95 ± 0.15 1.06 ± 0.58
HER2-Enriched 5 9.52 ± 3.37 1.03 ± 0.26 0.95 ± 0.63
Triple negative 13 12.27 ± 4.22 0.97± 0.23 1.28 ± 0.56

ER: Estrogen receptor, PR: Progesterone receptor PR, SUV: Standardized Uptake Value, ADC: Apparent Diffusion Coefficient, HER2: Human epidermal growth factor receptor 2

(a) Error bar showing difference between SUVmax and histopathological grades [p value - 0.003]. (b) Examples: Grade 1 invasive ductal carcinoma in right breast with minimal FDG uptake (SUV ~ 3.77). (c) Grade 2 carcinoma in right breast with moderate FDG uptake (SUV ~ 6.97). (d) Grade 3 carcinoma in left breast with marked FDG uptake (SUV ~ 11.7). SUV: Standardized Uptake Value, FDG: fluorodeoxyglucose.
Figure 1:
(a) Error bar showing difference between SUVmax and histopathological grades [p value - 0.003]. (b) Examples: Grade 1 invasive ductal carcinoma in right breast with minimal FDG uptake (SUV ~ 3.77). (c) Grade 2 carcinoma in right breast with moderate FDG uptake (SUV ~ 6.97). (d) Grade 3 carcinoma in left breast with marked FDG uptake (SUV ~ 11.7). SUV: Standardized Uptake Value, FDG: fluorodeoxyglucose.
(a) Error bar showing difference between SUVmax in different molecular subtypes [p value - 0.018]. Example: Patient with Triple negative molecular subtype – CT (b) and fused PET-CT (c) images showed markedly FDG avid irregular lesion with microlobulated margins in inner quadrant of right breast (SUV ~ 17.5) HP: HER2-Enriched, LA: Luminal A, LB: Luminal B, TN: Triple negative molecular subtype, SUV: Standardized Uptake Value, CT: Computed Tomography, PET-CT: positron emission tomography–computed tomography, FDG: fluorodeoxyglucose.
Figure 2:
(a) Error bar showing difference between SUVmax in different molecular subtypes [p value - 0.018]. Example: Patient with Triple negative molecular subtype – CT (b) and fused PET-CT (c) images showed markedly FDG avid irregular lesion with microlobulated margins in inner quadrant of right breast (SUV ~ 17.5) HP: HER2-Enriched, LA: Luminal A, LB: Luminal B, TN: Triple negative molecular subtype, SUV: Standardized Uptake Value, CT: Computed Tomography, PET-CT: positron emission tomography–computed tomography, FDG: fluorodeoxyglucose.

Statistically significant difference was seen in ADC values between positive and negative estrogen receptor tumors (P = 0.02) and different molecular subtypes (P = 0.009) [Figures 35; and Table 2]. Higher mean ADC values were seen in HER2-Enriched molecular subtype (1.03 ± 0.25) [Figure 3]. Lower mean ADC values were seen in ER-positive tumors (0.85 ± 0.16) and luminal A molecular subtype (0.79 ± 0.13) [Figure 4]. There was no statistically significant correlation between ADC values of different grades of malignancy, PR, and HER2 receptor status [Table 2].

(a) Error bar showing difference between ADC values in different molecular subtypes [p value - 0.009]. Example: 58-year-old postmenopausal female with HER2-Enriched molecular subtype AxT1. HP: HER2-Enriched, LA: Luminal A, LB: Luminal B, TN: Triple negative molecular subtype. (b) and post contrast (c) sequences show heterogeneous fibroglandular tissue with malignant lesion in inner quadrant of right breast showing heterogeneous enhancement, central non-enhancing areas and restricted diffusion (ADC values, 1.231 × 10−3 mm2/s) (d & e). ADC: Apparent Diffusion Coefficient, HER2: Human epidermal growth factor receptor 2.
Figure 3:
(a) Error bar showing difference between ADC values in different molecular subtypes [p value - 0.009]. Example: 58-year-old postmenopausal female with HER2-Enriched molecular subtype AxT1. HP: HER2-Enriched, LA: Luminal A, LB: Luminal B, TN: Triple negative molecular subtype. (b) and post contrast (c) sequences show heterogeneous fibroglandular tissue with malignant lesion in inner quadrant of right breast showing heterogeneous enhancement, central non-enhancing areas and restricted diffusion (ADC values, 1.231 × 10−3 mm2/s) (d & e). ADC: Apparent Diffusion Coefficient, HER2: Human epidermal growth factor receptor 2.
52-year-old female with Luminal A (ER+, PR+) molecular subtype and grade 2 invasive ductal carcinoma in right breast. Ax T1 (a) and post contrast (b) sequences shows malignant lesion in right breast with heterogeneous enhancement and satellite lesion anterior to the index lesion. The index lesion and the satellite nodule show restricted diffusion with low ADC values, 0.752 × 10−3 mm2/s (c & d). Also note the metastatic right axillary node with restricted diffusion (c). ER+: estrogen receptor-positive, PR+: progesterone receptor-positive, ADC: Apparent Diffusion Coefficient.
Figure 4:
52-year-old female with Luminal A (ER+, PR+) molecular subtype and grade 2 invasive ductal carcinoma in right breast. Ax T1 (a) and post contrast (b) sequences shows malignant lesion in right breast with heterogeneous enhancement and satellite lesion anterior to the index lesion. The index lesion and the satellite nodule show restricted diffusion with low ADC values, 0.752 × 10−3 mm2/s (c & d). Also note the metastatic right axillary node with restricted diffusion (c). ER+: estrogen receptor-positive, PR+: progesterone receptor-positive, ADC: Apparent Diffusion Coefficient.
53-years-old female with Triple negative molecular subtype and grade 3 carcinoma, Ax T1 (a) and post contrast (b) sequences show heterogeneous fibroglandular tissue with heterogeneously enhancing irregular micro lobulated lesion in 6 O’ clock position of right breast. The lesion shows restricted diffusion with low ADC values, 0.731 × 10−3 mm2/s (c&d). ADC: Apparent Diffusion Coefficient.
Figure 5:
53-years-old female with Triple negative molecular subtype and grade 3 carcinoma, Ax T1 (a) and post contrast (b) sequences show heterogeneous fibroglandular tissue with heterogeneously enhancing irregular micro lobulated lesion in 6 O’ clock position of right breast. The lesion shows restricted diffusion with low ADC values, 0.731 × 10−3 mm2/s (c&d). ADC: Apparent Diffusion Coefficient.

Twenty-five out of 53 patients in our study had heterogenous or extreme fibroglandular pattern in which 24 patients (95%) had a higher grade of malignancy. Among the patients with marked background parenchymal enhancement (5 out of 53), 80% of them had Grade 3 malignancies. There was no statistically significant correlation between background SUV of the contralateral breast, with the grades of malignancy, receptor status, or molecular subtypes.

DISCUSSION

Tumor grade, receptor status, and molecular subtypes are important histological prognostic factors. Higher grade of malignancy and triple negative molecular subtype are aggressive with poor prognosis.

18F-fluorodeoxyglucose PET detects enhanced glycolysis of cancer cells, which is primarily used for staging, response assessment and identifying disease recurrence. FDG uptake is expressed in a quantitative parameter, that is, SUVmax, and it carries clinical as well as biological information. In our study, higher SUV values were seen in tumors with Grade 3, ER or PR negative and triple negative molecular subtype. These results are similar to those of few previously published studies such Groheux et al,[13] Nakajo et al,[22] Ueda et al,[34] Choi et al,[35] Abubakar et al.[36]

In our study, no correlation was found between grade of tumors and ADC values. In accordance with Yoshikawa et al.,[37] the ADC value depends on a number of factors including cell density, the spatial organization, and characteristics of the cells such as wall or nuclear size and the type of the stroma. It is not unusual to find high-grade invasive tumors with ADC values higher than expected. It is likely that these types of tumors have a microstructure that promotes water diffusion. Kim et al.,[25] concluded that the ADC value was a helpful parameter in detecting malignant breast tumors, but it could not predict patient prognosis.

In our study, statistically significant variation in ADC values among various molecular subtypes. Higher ADC values were seen in HER2-Enriched tumors. Similar results were seen in Horvat et al.[38] and Kim et al.[32] Kim et al.[32] stated, it is known that HER2 expression increases angiogenesis which leads to increase in tumor vascularity. These new vessels are larger and discontinuous that increases the extracellular fluid volume thereby increasing the ADC values.[19] Low ADC values were seen in luminal A subtype and ER-positive tumors were consistent with the results of Horvat et al.[38] as these tumors have low neovascularity and high cellularity. It is important to differentiate luminal and non-luminal tumors for the reason that the luminal tumors require endocrine therapy rather than a cytotoxic chemotherapy. Horvat et al.[38] expressed that in future the improvements in DWI technology may increase the accuracy of ADC metrics and it can have clinical applicability in the preoperative classification of tumor subtypes.

However, there was no statistically significant variation in ADC values between progesterone/HER2 receptor-positive and negative lesions in our study. Similar results were also observed with Nakajo et al.[22] and Kim et al.[25]

Larger number of patients with heterogeneous fibroglandular pattern had higher grade of malignancies. These results were in agreement with the previously published studies by McCormack et al.,[14] and Boyd et al.,[15] which proved that the risk of malignancy is higher in patients with denser breast.

This study has some limitations. It was a short-duration prospective study with a relatively low number of patients. Heterogeneous sample of patients were examined that showed uneven distribution of histologic (low number of lobular carcinoma and Grade 1 tumors) and molecular subtypes (low number of HER2-Enriched type) which can influence the significance of the results

CONCLUSION

PET-CT and MRI can be used as a complementary imaging tool in evaluating the patient with breast carcinoma for noninvasive assessment of the aggressiveness, and biological characteristics of tumor such as grade, hormone receptor status, and to differentiate molecular subtypes.

Our study showed higher SUV values in Grade III, ER- or PR-negative and triple-negative tumors. Therefore, in essence, the SUV values obtained from 18F-FDG PET-CT shows a positive correlation with the aggressiveness of the tumor. ADC values helps to analyze the cellularity and neo angiogenesis of the tumor

Ethical Approval

The author(s) declare that they have taken the ethical approval from IEC (ECR/37/Inst/TN/2013/RR-16).

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

REFERENCES

  1. , , , , , . Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359-86.
    [CrossRef] [PubMed] [Google Scholar]
  2. , , , , , . Diagnosis of second breast cancer events after initial diagnosis of early stage breast cancer. Breast Cancer Res Treat. 2010;124:863-73.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
  3. , , , , , . Family history, age, and risk of breast cancer. Prospective data from the Nurses’ Health Study [published correction appears in JAMA 1993;270:1548] JAMA. 1993;270:338-43.
    [PubMed] [Google Scholar]
  4. , , , . Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science. 2003;302:643-6.
    [CrossRef] [PubMed] [Google Scholar]
  5. , . Breast cancer and hormone replacement therapy (HRT): Collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer. Breast Cancer Res. 2002;4(Suppl 1):3.
    [Google Scholar]
  6. , , , , , . Causes of cancer in the world: Comparative risk assessment of nine behavioural and environmental risk factors. Lancet. 2005;366:1784-93.
    [CrossRef] [PubMed] [Google Scholar]
  7. , , , , , . Systematic review: Surveillance for breast cancer in women treated with chest radiation for childhood, adolescent, or young adult cancer. Ann Intern Med. 2010;152:444-W154.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
  8. , , , , . Spectrum of breast cancer in Asian women. World J Surg. 2007;31:1031-40.
    [CrossRef] [PubMed] [Google Scholar]
  9. . The Indian scene. J Clin Oncol. 2001;19(18 Suppl):106S-11S.
    [Google Scholar]
  10. , , , , , . Strategies for subtypes—Dealing with the diversity of breast cancer: Highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol. 2011;22:1736-47.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
  11. , , , , , . Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003;100:8418-23.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
  12. , , , , , . Molecular portraits of human breast tumours. Nature. 2000;406:747-52.
    [CrossRef] [PubMed] [Google Scholar]
  13. , , , , , . Correlation of high 18F-FDG uptake to clinical, pathological and biological prognostic factors in breast cancer. Eur J Nucl Med Mol Imaging. 2011;38:426-35.
    [CrossRef] [PubMed] [Google Scholar]
  14. , . Breast density and parenchymal patterns as markers of breast cancer risk: A meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15:1159-69.
    [CrossRef] [PubMed] [Google Scholar]
  15. , , , , , . Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356:227-36.
    [Google Scholar]
  16. , , . Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer. 1989;63:181-7.
    [Google Scholar]
  17. , , , , , . The effect of tumor size and lymph node status on breast carcinoma lethality. Cancer. 2003;98:2133-43.
    [CrossRef] [PubMed] [Google Scholar]
  18. , , , , , . Prognostic significance of lymphatic vessel invasion diagnosed by D2-40 in Chinese invasive breast cancers. Medicine (Baltimore). 2017;96:e8490.
    [Google Scholar]
  19. , , , , , . Prognostic and predictive impact of central necrosis and fibrosis in early breast cancer: Results from two International Breast Cancer Study Group randomized trials of chemoendocrine adjuvant therapy. Breast Cancer Res Treat. 2010;121:211-8.
    [Google Scholar]
  20. , , , , , . The importance of multifocal/multicentric tumor on the disease-free survival of breast cancer patients: Single center experience. Am J Clin Oncol. 2012;35:580-6.
    [PubMed] [Google Scholar]
  21. , , , , , . The presence of an extensive intraductal component following a limited excision correlates with prominent residual disease in the remainder of the breast. J Clin Oncol. 1990;8:113-8.
    [PubMed] [Google Scholar]
  22. , , , , , . FDG PET/CT and diffusion-weighted imaging for breast cancer: Prognostic value of maximum standardized uptake values and apparent diffusion coefficient values of the primary lesion. Eur J Nucl Med Mol Imaging. 2010;37:2011-20.
    [CrossRef] [PubMed] [Google Scholar]
  23. , , , , , . Predicting the prognoses of breast carcinoma patients with positron emission tomography using 2-deoxy-2-fluoro[18F]-D-glucose. Cancer. 1998;82:2227-34.
    [PubMed] [Google Scholar]
  24. , , , , , . Standardized uptake values for breast carcinomas assessed by fluorodeoxyglucose-positron emission tomography correlate with prognostic factors. Am Surg. 2007;73:1151-7.
    [PubMed] [Google Scholar]
  25. , , , , . Diffusion-weighted imaging of breast cancer: Correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging. 2009;30:615-20.
    [CrossRef] [PubMed] [Google Scholar]
  26. , , , , , . Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging. 2002;16:172-8.
    [CrossRef] [PubMed] [Google Scholar]
  27. , , , , . In vivo diffusion-weighted MRI of the breast: Potential for lesion characterization. J Magn Reson Imaging. 2002;15:693-704.
    [CrossRef] [PubMed] [Google Scholar]
  28. , , , , , . Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol. 2012;85:e474-e479.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
  29. , , , , , . Quantitative apparent diffusion coefficient as a noninvasive imaging biomarker for the differentiation of invasive breast cancer and ductal carcinoma in situ. Invest Radiol. 2015;50:95-100.
    [CrossRef] [PubMed] [Google Scholar]
  30. , , , , , . Diffusion-weighted imaging in breast cancer: Relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol. 2010;65:1005-12.
    [CrossRef] [PubMed] [Google Scholar]
  31. , , , , , . Diffusion magnetic resonance imaging in breast cancer characterisation: Correlations between the apparent diffusion coefficient and major prognostic factors. Radiol Med. 2015;120:268-76.
    [CrossRef] [PubMed] [Google Scholar]
  32. , , , , , . Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma. J Magn Reson Imaging. 2015;42:1666-78.
    [CrossRef] [PubMed] [Google Scholar]
  33. , , . Established breast cancer risk factors and risk of intrinsic tumor subtypes. Biochim Biophys Acta. 2015;1856:73-85.
    [CrossRef] [PubMed] [Google Scholar]
  34. , , , , , . Clinicopathological and prognostic relevance of uptake level using 18F-fluorodeoxyglucose positron emission tomography/computed tomography fusion imaging (18F-FDG PET/CT) in primary breast cancer. Jpn J Clin Oncol. 2008;38:250-8.
    [CrossRef] [PubMed] [Google Scholar]
  35. , , , , , . Diffusion-weighted imaging and FDG PET/CT: Predicting the prognoses with apparent diffusion coefficient values and maximum standardized uptake values in patients with invasive ductal carcinoma. World J Surg Oncol. 2012;10:126.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
  36. , , . Correlation of maximum standardized uptake values in 18F-fluorodeoxyglucose positron emission tomography-computed tomography scan with immunohistochemistry and other prognostic factors in breast cancer. Indian J Nucl Med. 2019;34:10-3.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
  37. , , , , , . Comparison of breast cancer detection by diffusion-weighted magnetic resonance imaging and mammography. Radiat Med. 2007;25:218-23.
    [CrossRef] [PubMed] [Google Scholar]
  38. , , , , , . Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer. J Magn Reson Imaging. 2019;50:836-46.
    [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
Show Sections