Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer

The goal of medical imaging is to acquire and display images of human anatomy and function such that they can be optimally interpreted by a trained observer, e.g., a radiologist. Start-of-art medical image quality is measured by the performance of an observer on a given clinical task. Since psychophysical studies are resource intensive, model observers are widely used as a surrogate in task-based assessment of image quality. Model observers are typically designed to detect at most one abnormality, e.g., a single lesion. However, in clinical practice, there may be multiple abnormalities in a single set of images, which can have a significant impact on treatment planning and outcomes. For example, patients with multifocal and multicentric breast cancer (MFMC), i.e., the presence of two or more tumor foci within the same breast, are more likely to undergo mastectomy rather than breast conservation therapy. Detecting multiple breast tumors is challenging because the prevalence of tumors varies significantly across breast regions, and radiologists do not know the number or location of tumors a priori. The vision of this dissertation is that digital breast tomosynthesis (DBT) has the potential to improve the detection of MFMC, and may offer advantages such as fewer false-positive findings, lower cost, and better accessibility. This dissertation focuses on the design and applications of a model observer to optimize DBT system geometries for detection of multiple breast tumors. This is significant and innovative because prior efforts to optimize DBT image quality only considered unifocal breast cancer scenarios. We highlight the following two main aspects of contributions in this dissertation: (1) We have developed a novel model observer that detects multiple abnormalities in anatomical backgrounds. (2) We have employed the extended 3D multi-lesion model observer to identify DBT system geometries that are most effective for the detection of MFMC. Our results demonstrate that the presence of more than one tumor present distinct challenges to DBT optimization, and that DBT geometries that yield images that are informative for the task of detecting unifocal breast cancer may not necessarily be informative for the task of detecting MFMC. We are validating the clinical relevance of our model observer studies with an ongoing human observer study with experienced breast imaging radiologists.Electrical and Computer Engineerin

Advancing combined radiological and optical scanning for breast-conserving surgery margin guidance

Breast cancer is one of the most common types of cancer worldwide, and standard-of-care for early-stage disease typically involves a lumpectomy or breast-conserving surgery (BCS). BCS involves the local resection of cancerous tissue, while sparring as much healthy tissue as possible. State-of-the-art methods for intraoperatively evaluating BCS margins are limited. Approximately 20% of BCS cases result in a tissue resection with cancer at or near the resection surface (i.e., a positive margin). A two-fold increase in ipsilateral breast cancer recurrence is associated with the presence of one or more positive margins. Consequently, positive margins often necessitate costly re-excision procedures to achieve a curative outcome. X-ray micro-computed tomography (CT) is emerging as a powerful ex vivo specimen imaging technology, as it provides robust three-dimensional sensing of tumor morphology rapidly. However, X-ray attenuation lacks contrast between soft tissues that are important for surgical decision making during BCS. Optical structured light imaging, including spatial frequency domain imaging and active line scan imaging, can act as adjuvant tools to complement micro-CT, providing wide field-of-view, non-contact sensing of relevant breast tissue subtypes on resection margins that cannot be differentiated by micro-CT alone. This thesis is dedicated to multimodal imaging of BCS tissues to ultimately improve intraoperative BCS margin assessment, reducing the number of positive margins after initial surgeries and thereby reducing the need for costly follow-up procedures. Volumetric sensing of micro-CT is combined with surface-weighted, sub-diffuse optical reflectance derived from high spatial frequency structured light imaging. Sub-diffuse reflectance plays the key role of providing enhanced contrast to a suite of normal, abnormal benign, and malignant breast tissue subtypes. This finding is corroborated through clinical studies imaging BCS specimen slices post-operatively and is further investigated through an observational clinical trial focused on combined, intraoperative micro-CT and optical imaging of whole, freshly resected BCS tumors. The central thesis of this work is that combining volumetric X-ray imaging and sub-diffuse optical scanning provides a synergistic multimodal imaging solution to margin assessment, one that can be readily implemented or retrofitted in X-ray specimen imaging systems and that could meaningfully improve surgical guidance during initial BCS procedures

Beyond mammography : an evaluation of complementary modalities in breast imaging

Breast cancer is the main cause of cancer death among women worldwide and the goal of mammography screening is to reduce breast cancer-specific mortality. The reduction of the sensitivity of mammography for detecting cancer among women with dense breasts requires the use of complementary methods for this subset of women. Three of the projects in this thesis examine the performance of such complementary methods and a fourth study investigates the association between the biomarker BPE (background parenchymal enhancement) and risk factors for breast cancer. In study 1, we prospectively compared the sensitivity and specificity of Automated Breast Volume Scanner (ABVS) with handheld ultrasound for detection of breast cancer among women with a suspicious mammographic finding who were recalled after attending the population-based mammography screening program. We performed both methods on 113 women and found 26 malignant lesions. Analysis was performed in two categories: breasts with a suspicious screening mammography and breasts with a negative screening mammography. In the first category (n=118) the sensitivity of both methods was 88% (p=1.0), the specificity of handheld ultrasound was 93.5 % and ABVS was 89.2%. The difference in specificity was not statistically significant (p=0.29). For breasts without a suspicious mammographic finding, the sensitivity of handheld ultrasound and ABVS was 100% (p=1.0), the specificity was 100% and 94.1% respectively. The difference in specificity was statistically significant (p=0.03). In summary, ABVS has similar sensitivity to handheld ultrasound, but lower specificity in breasts with a negative mammogram. In study 2, we explored the incremental cancer detection rate when adding a threedimensional infrared imaging (3DIRI) score to screening mammography among women with dense breasts (Volpara volumetric density >6 % on the previous mammography examination) who attended the population-based mammography screening program. Women with a negative mammogram and positive 3DIRI score were triaged for a DCEMRI examination to verify the presence of cancer. Of 1727 participants, 7 women had a mammography-detected breast cancer. Among women with a negative mammogram and a positive infrared imaging (n=219), an additional 6 cancers in 5 women were detected on MRI resulting in an incremental cancer detection rate of 22.5 per 1000. Among women with a negative mammography and infrared examination, one woman was diagnosed with breast cancer during the two-year follow-up. The study does not provide information on the proportion of cancers that might have been detected had MRI been performed among women with a negative mammogram and 3DIRI score. Consequently, this study does not shed light on the diagnostic accuracy of infrared imaging or whether using an infrared risk score is the optimal method for identifying women who would benefit from additional imaging modalities. In study 3, we used MRI examinations of study 2 among women without breast cancer (n=214) to explore the association between BPE at DCE-MRI and a large array of risk factors for breast cancer. Thanks to the Karma database, we had unique access to data from self-reporting questionnaires on risk factors. BPE and mammographic density were assessed visually by three radiologists and BPE was further dichotomized into low and high. We created categorical variables for other risk factors. We calculated the univariable associations between BPE and each risk factor and fitted an adjusted logistic regression model. In the adjusted model, we found a negative association with age (p=0.002), and a positive association with BMI (p=0.03). There was a statistically significant association with systemic progesterone (p=0.03) but since only five participants used progesterone preparations, the result is uncertain. Although the likelihood for high BPE increased with increase in mammographic density, the association was not statistically significant (p=0.23). We were able to confirm earlier findings that BPE is associated with age, BMI and progesterone, but we could not find an association with other risk factors for breast cancer. In study 4, we compared the diagnostic accuracy, reading-time, and inter-rater agreement of an abbreviated protocol (aMRI) to the routine full protocol (fMRI) of contrast-enhanced breast MRI. The MRI examinations were performed before biopsy and among women who were not part of a surveillance program due to an increased familial risk of breast cancer. Analysis was performed on a per breast basis. Aggregated across three readers, the sensitivity and specificity were 93.0% and 91.7% for aMRI, and 92.0% and 94.3% for the fMRI. Using a generalized estimating equations approach to compare the two protocols, the difference in sensitivity was not statistically significant (p=0.840), and the difference in specificity was significant (p=0.003). There was a statistically significant difference in average reading time of 67 seconds for aMRI and 126 seconds for the fMRI (p= 0.000). The inter-rater agreement was 0.79 for aMRI and 0.83 for fMRI. We were able to demonstrate that the abbreviated protocol has similar sensitivity to the full protocol even if MRI is performed before biopsy and the images lack telltale signs of malignancy. In conclusion, this thesis provides new knowledge about the biomarker BPE, broadens our knowledge on the diagnostic accuracy of two different imaging modalities and highlights the importance of good study design for diagnostic accuracy studies

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

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