Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

Novel infrared and Raman spectroscopic imaging for the elucidation of specific changes in breast microcalcifications

Breast cancer is the second most common cause of death from cancer in women, accounting for more than 1 million deaths globally per year. Current detection is based on X-ray mammographic screening, which involves the use of ionising radiation with potentially detrimental effects, or MRI scans, which have limited spatial resolution. The presence of microcalcifications in breast tissue has been associated with malignant disease. Unfortunately, X-ray mammography and MRI scanning techniques are not able to discriminate between microcalcifications from a benign lesion and those from a malignant lesion. The aim of this project was to use optical techniques based on vibrational spectroscopy, such as Fourier Transform Infrared (FTIR) absorption and Raman scattering, which are non-destructive, label-free and chemically specific, to investigate the composition of microcalcifications in breast tissue for augmented diagnostics and improved outcome for the patient. This work involved the characterisation of mineral standards of the type that can be found in the breast, in order to identify the precise composition of the microcalcifications. A series of calcium hydroxyapatite (Hap) compounds was used for calibration of the micro-FTIR and Raman spectra. The ratio of carbonate-to-phosphate band intensity for each individual Hap powder was determined and the data were used to assess the level of carbonate substitution in each breast tissue biopsy. In parallel, the analysis of potential precursor mineral phases (namely octacalcium phosphate and amorphous calcium phosphate) revealed similar features to Hap in both FTIR and Raman spectra, which can be translated to the biopsy samples. The accessibility to diverse panels of breast tissue sections (frozen and paraffin-embedded) was a great opportunity to test different approaches. A deparaffinisation protocol was applied to a set of samples for Raman analysis and the process was found not to affect the microcalcification composition. The FTIR analysis of the frozen tissues provided information on the carbonate peak in the short wavelength range (1500-1400 cm-1), which normally contains a strong contribution from paraffin in standard histological specimens. The study of breast tissue sections showed the heterogeneity in composition of microcalcifications between different samples from the same stage of pathology in terms of protein, lipid - which is usually not observed in formalin-fixed paraffin-preserved (FFPE) sections - and carbonate content. The mineralisation of the MDA-MB-231 breast cell line induced by two osteogenic agents: inorganic phosphate (Pi) and -Glycerophosphate (G) was investigated using Raman micro-spectroscopy. The uptake of osteogenic agent induced a faster mineralisation for cells cultured with a medium supplemented in Pi (day 3) than G (day 11). A shift (± 3 cm-1) of the phosphate peak at 956 cm-1 in the Raman spectra was apparent when the culture medium was supplemented with G, indicating the presence of precursor phase (octacalcium phosphate) during Hap crystal formation. New IR technologies such as bright laser sources e.g. quantum cascade laser (QCL) open possibilities for the analysis of biological samples. They allowed us to achieve a better signal-to-noise ratio than Globar thermal sources used in traditional FTIR systems, particularly on optically dense samples such as calcifications. The ability of selecting specific incident wavelengths allows significant improvements in the acquisition time. This work illustrates for the first time the identification of microcalcifications using a QCL source in the long wavelength range coupled to an upconversion system with a silicon detector for efficient sensing. The upconverted images showed a good agreement with the micro-FTIR images. Vibrational spectroscopy has been shown to be a powerful tool for discrimination of mineral species in breast calcification. These techniques can provide complementary information for the pathologist to be able to classify breast pathologies - benign, ductal carcinoma in situ (DCIS) and invasive cancer - with higher accuracy.European Commissio

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Molecular aspects of thyroid calcification

In thyroid cancer, calcification is mainly present in classical papillary thyroid carcinoma (PTC) and in medullary thyroid carcinoma (MTC), despite being described in benign lesions and in other subtypes of thyroid carcinomas. Thyroid calcifications are classified according to their diameter and location. At ultrasonography, microcalcifications appear as hyperechoic spots = 1 mm in diameter and can be named as stromal calcification, bone formation, or psammoma bodies (PBs), whereas calcifications > 1 mm are macrocalcifications. The mechanism of their formation is still poorly understood. Microcalcifications are generally accepted as a reliable indicator of malignancy as they mostly represent PBs. In order to progress in terms of the understanding of the mechanisms behind calcification occurring in thyroid tumors in general, and in PTC in particular, we decided to use histopathology as the basis of the possible cellular and molecular mechanisms of calcification formation in thyroid cancer. We explored the involvement of molecules such as runt-related transcription factor-2 (Runx-2), osteonectin/secreted protein acidic and rich in cysteine (SPARC), alkaline phosphatase (ALP), bone sialoprotein (BSP), and osteopontin (OPN) in the formation of calcification. The present review offers a novel insight into the mechanisms underlying the development of calcification in thyroid cancer.This research was funded by FEDER—Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020—Operacional Programme for Competitiveness and Internationalization (POCI), Portugal 2020, and by Portuguese funds through FCT—Fundação para a Ciência e a Tecnologia/Ministério daCiência, Tecnologia e Inovação in the framework of the project “Institute for Research and Innovation in Health Sciences” Funding: This research was funded by FEDER—Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020—Operacional Programme for Competitiveness and Internationalization (POCI), Portugal 2020, and by Portuguese funds through FCT—Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Inovação in the framework of the project “Institute for Research and Innovation in Health Sciences” (POCI-01-0145-FEDER-007274). Additional funding by the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization—COMPETE2020; Portuguese national funds via FCT, under project POCI-01-0145-FEDER-016390: CANCEL STEM; and from the FCT, under the project POCI-01-0145-FEDER-031438: The other faces of telomerase: Looking beyond tumour immortalization (PDTC/MED_ONC/31438/2017). J.V. is funded with a research contract (CEECIND/00201/2017) by Fundação para a Ciência e a Tecnologia, Ministério da Ciência, Tecnologia e Ensino Superior (FCT). This research was funded by FEDER?Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020?Operacional Programme for Competitiveness and Internationalization (POCI), Portugal 2020, and by Portuguese funds through FCT?Funda??o para a Ci?ncia e a Tecnologia/Minist?rio da Ci?ncia, Tecnologia e Inova??o in the framework of the project ?Institute for Research and Innovation in Health Sciences? (POCI-01-0145-FEDER-007274). Additional funding by the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization?COMPETE2020; Portuguese national funds via FCT, under project POCI-01-0145-FEDER-016390: CANCEL STEM; and from the FCT, under the project POCI-01-0145-FEDER-031438: The other faces of telomerase: Looking beyond tumour immortalization (PDTC/MED_ONC/31438/2017). J.V. is funded with a research contract (CEECIND/00201/2017) by Funda??o para a Ci?ncia e a Tecnologia, Minist?rio da Ci?ncia, Tecnologia e Ensino Superior (FCT)