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BREAST CANCER DIAGNOSIS: A COMPARATIVE STUDY OF METHODS
BASED ON SENSITIVITY AND SPECIFICITY
Muthulingam Sharavanan Gayathri Srikanth*, Kalash Dwivedi, Vinayak Koli , Jainil
Sejpal Karthigeyan S, Karthik Shrishail Vastrad, Rohan Rajendra Patil, Surajit Bose
Students of Tashkent medical Academy, Tashkent Uzbekistan .
Abstract:
This study compares different methods for diagnosing breast cancer, focusing on
their sensitivity, specificity, and clinical usefulness. Using the PICO framework, we
reviewed key studies published from 2002 to 2024, sourced from databases like PubMed,
Scopus, and Google Scholar. In total, we analysed 8 studies, covering a range of diagnostic
techniques. These methods include traditional approaches like Core Needle Biopsy (CNB)
and Fine Needle Aspiration (FNA), as well as more advanced methods such as Abbreviated
MRI (ABB-MRI), image segmentation algorithms, Raman and infrared spectroscopy, blood-
based proteomics, and cytometric testing. The findings showed that CNB had the highest
diagnostic performance overall. However, ABB-MRI and some molecular techniques
offered strong non-invasive alternatives. Computational tools, while promising, still need
more validation for widespread use. In conclusion, no single diagnostic method stood out as
the best for all cases. Instead, a combination of different approaches, tailored to each
patient’s specific situation, seems to be the most accurate and efficient strategy for detecting
breast cancer.
Keywords:
Breast cancer, diagnosis, sensitivity, specificity, core needle biopsy, MRI,
spectroscopy, proteomics, image segmentation, SCM test.
INTORDUCTION
In breast cancer diagnosis, the clinical relevance of sensitivity and specificity cannot be
overstated, as these metrics directly impact patient outcomes by guiding therapeutic
decisions and minimizing diagnostic errors. Sensitivity ensures that malignant cases are
correctly identified, crucial for initiating timely treatment and preventing disease
progression. Specificity, on the other hand, reduces false positives, sparing patients from
unnecessary anxiety, biopsies, and interventions. The ideal diagnostic method balances both,
adapting to clinical needs whether in screening asymptomatic populations or confirming
malignancy in symptomatic patients. With evolving technologies—from imaging modalities
like MRI to molecular and computational diagnostics—emphasis on sensitivity and
specificity helps clinicians choose the most effective approach for each scenario. As breast
cancer presentations and patient risk profiles vary, the nuanced application of these
performance metrics ensures more accurate, patient-centred care and supports the broader
goals of early detection and personalized oncology.
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Methodology
This study employs the PICO (Population, Intervention, Comparison, Outcome) framework
to structure its clinical inquiry into the diagnostic accuracy of various breast cancer detection
methods. The
Population (P)
focuses on women undergoing diagnostic evaluation for
suspected breast cancer. The
Intervention (I)
includes a range of diagnostic methods such
as Core Needle Biopsy (CNB), Fine Needle Aspiration (FNA), Abbreviated MRI (ABB-
MRI), image segmentation techniques, spectral diagnostics, and blood-based proteomics.
The
Comparison (C)
contrasts the sensitivity, specificity, and diagnostic accuracy of these
methods. The
Outcome (O)
evaluates each method’s effectiveness in terms of these
diagnostic parameters.
A systematic literature review was conducted using major academic databases including
PubMed
,
Google Scholar
,
Scopus
,
Web of Science
, and the
Cochrane Library
. Search
terms included combinations of "breast cancer", "diagnosis", "sensitivity", "specificity",
"accuracy", "MRI", "biopsy", "spectroscopy", and "machine learning". Boolean operators
(AND, OR) and MeSH terms were used to optimize search results. After removing
duplicates and screening for relevance, 15 initial articles were shortlisted. From these,
10
studies
were selected based on inclusion criteria: English-language full-text availability,
quantitative diagnostic data, publication between 2002 and 2024, and clinical relevance.
Study
Method
Key Finding
Advantages
Disadvantages
Ibrahim et
al. (2024)
CNB
vs
FNA
CNB more sensitive; both
highly specific
Highly accurate; standard
for histological confirmation
More
invasive;
requires
trained
personnel
He et al.
(2023)
ABB-MRI High
sensitivity/specificity;
good for high-risk women
Non-invasive;
useful
in
dense breast tissue
Limited
availability;
requires MRI setup
Kuşç
u & Erol
(2022)
K-Mean
Clustering
Effective in image-based
diagnostics
Automates tumor detection;
useful in digital diagnostics
Dependent on quality
of image input and
algorithm
Kuşç
u & Erol
(2022)
Otsu
Threshold
ing
Comparable to K-Mean;
slightly less accurate
Simple image processing
technique;
effective
in
segmentation
Lower accuracy than
K-Mean; sensitive to
pixel noise
MartÃnez
Romo et
al. (2015)
Raman
Spectrosc
opy
Perfect sensitivity and
specificity in trial
Real-time,
intraoperative
use; exceptional accuracy
Requires spectral tools
and expertise
Backhaus
et
al.
(2010)
IR
Spectrosc
opy
Highly accurate in serum-
based detection
Non-invasive
serum
analysis;
excellent
diagnostic potential
May face overlap in
spectral patterns with
other diseases
Khoroush
i et al.
(2024)
Ultrasoun
d-Guided
FNA
Accurate
for
nodal
metastasis detection
Reliable pre-surgical tool for
nodal assessment
May
miss
micrometastases;
operator-dependent
Drukier et
al. (2006)
Multiphot
on
Proteomic
s
Effective
blood-based
screening tool
Early detection from blood;
high accuracy in screening
High cost and complex
data interpretation
Klein et
al. (2002)
SCM Test Useful in early malignancy
detection
Useful for distinguishing
benign vs malignant in early
stage
Not
widely
used;
needs more clinical
validation
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The 10 studies chosen represent a diverse spectrum of diagnostic methodologies, including
traditional pathological assessments (CNB and FNA), imaging-based techniques (ABB-
MRI), computational image analysis (K-means and Otsu methods), molecular diagnostics
(Raman and IR spectroscopy), node-focused cytology (ultrasound-guided FNA), biomarker-
based assays (multiphoton proteomics), and antigen-responsive cytometry (SCM test). These
studies provide robust comparative insights into the diagnostic landscape, facilitating
evidence-based evaluation of breast cancer detection strategies across clinical and
technological dimensions.
Results
Table.1. Comparative Performance of Breast Cancer Diagnostic Methods with
Sensitivity, Specificity, Key Findings, and Practical Considerations
The comparative analysis of the 8 selected studies revealed significant variability in
diagnostic performance among the evaluated methods, particularly in terms of sensitivity,
specificity, and overall clinical utility. Core Needle Biopsy (CNB) consistently
demonstrated superior diagnostic performance with a sensitivity of 88.1% and specificity of
97.2%, making it a preferred standard for tissue-based confirmation. In contrast, Fine Needle
Aspiration (FNA), while still widely used due to its simplicity and accessibility, showed
lower sensitivity at 68.6% but maintained a high specificity of 96.1%. Abbreviated MRI
(ABB-MRI) emerged as an effective imaging technique, with pooled sensitivity and
specificity of 87% and 90%, respectively, proving valuable especially in high-risk and dense
breast populations. Computational image analysis methods such as K-Mean Clustering and
Otsu Thresholding showed promising accuracy rates ranging from 84% to 89%, supporting
their role as supplementary tools in automated diagnostics. Molecular-based diagnostics also
yielded highly favorable outcomes: Raman spectroscopy achieved 100% sensitivity and
specificity in select trials, while infrared spectroscopy reported up to 98% sensitivity and
100% specificity in serum-based cancer detection. Ultrasound-guided FNA of axillary
lymph nodes showed strong diagnostic accuracy with 93.6% sensitivity and 96.3%
specificity, aiding in preoperative staging. Proteomic approaches using multiphoton
detection in blood samples reported approximately 95% for both sensitivity and specificity,
Study
Method
Sample Size
Sensitivity
Specificity
Accuracy
Ibrahim et al. (2024)
CNB vs FNA
1177 patients
88.1%
/
68.6%
97.2%
/
96.1%
-
He et al. (2023)
ABB-MRI
18 studies (meta-
analysis)
87%
90%
-
Kuşçu & Erol
(2022)
K-Mean
Clustering
9 images
89%
87%
87%
Kuşçu & Erol
(2022)
Otsu
Thresholding
9 images
84%
87%
84%
MartÃnez Romo et al.
(2015)
Raman
Spectroscopy
Unknown
100%
100%
100%
Backhaus et al. (2010)
IR Spectroscopy
3119 samples
98%
100%
91%-
100%
Khoroushi et al. (2024) Ultrasound-
Guided FNA
102 patients
93.60%
96.30%
95.10%
Drukier et al. (2006)
Multiphoton
Proteomics
250 samples + 95
controls
95%
95%
-
Klein et al. (2002)
SCM Test
137 patients
81%
85%
-
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highlighting the potential for non-invasive early detection. Lastly, cytometric methods like
the SCM test using tumor antigens achieved 81% sensitivity and 85% specificity, indicating
potential utility in early malignancy screening. Overall, these findings emphasize that while
traditional methods remain essential, integrating newer imaging, spectral, and blood-based
technologies can enhance diagnostic precision and support personalized breast cancer
management.
Discussion
This review highlights the variability and complementary strengths of multiple diagnostic
methods used in breast cancer detection. Core Needle Biopsy (CNB) emerges as a gold
standard due to its high sensitivity and specificity, making it ideal for confirming
malignancy. Fine Needle Aspiration (FNA), while less sensitive, remains valuable in
resource-limited settings or for quick preliminary assessments. Abbreviated MRI (ABB-
MRI) provides a non-invasive, high-performance imaging option, particularly beneficial for
women with dense breast tissue or elevated genetic risk profiles. Emerging computational
tools, such as K-Mean clustering and Otsu thresholding, offer promising automation in
imaging interpretation, though further validation is required for clinical integration.
Molecular diagnostics—like Raman and infrared spectroscopy—deliver exceptional
accuracy and are gaining traction as intraoperative tools or supplementary screening
measures. Blood-based methods, including multiphoton proteomics and antigen-driven SCM
testing, offer innovative, minimally invasive pathways for early detection and monitoring.
Collectively, these studies underscore the importance of context-specific application, where
a method’s sensitivity, specificity, and feasibility guide its clinical utility. The integration of
these diagnostic tools into a multimodal strategy may enhance early detection, reduce
diagnostic errors, and promote individualized patient care.
Conclusion
Breast cancer diagnosis is evolving rapidly, driven by technological innovation and the
demand for higher diagnostic accuracy. No single method is universally optimal; each has its
advantages and limitations. Core Needle Biopsy remains the most reliable for tissue
confirmation, while imaging modalities like ABB-MRI provide excellent non-invasive
alternatives. Spectral and molecular methods push the boundaries of diagnostic precision,
and computational tools open avenues for efficient and reproducible analysis. As sensitivity
and specificity remain central to diagnostic performance, their careful evaluation ensures
that the chosen method aligns with patient needs and clinical objectives. A tailored,
multimodal diagnostic approach that leverages the strengths of each method offers the best
pathway to early detection, reduced morbidity, and improved outcomes in breast cancer care.
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