Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements

Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.This work has been supported by the Spanish Government through CYCIT projects DA2TOI (FIS2010-19860), TFS (TEC2010-20224-C02-02) and Alma’s Eguizabal PhD Grant (FPU12/04130) and by Dartmouth College

SURGICAL NAVIGATION AND AUGMENTED REALITY FOR MARGINS CONTROL IN HEAD AND NECK CANCER

I tumori maligni del distretto testa-collo rappresentano un insieme di lesioni dalle diverse caratteristiche patologiche, epidemiologiche e prognostiche. Per una porzione considerevole di tali patologie, l’intervento chirurgico finalizzato all’asportazione completa del tumore rappresenta l’elemento chiave del trattamento, quand’anche esso includa altre modalità quali la radioterapia e la terapia sistemica. La qualità dell’atto chirurgico ablativo è pertanto essenziale al fine di garantire le massime chance di cura al paziente. Nell’ambito della chirurgia oncologica, la qualità delle ablazioni viene misurata attraverso l’analisi dello stato dei margini di resezione. Oltre a rappresentare un surrogato della qualità della resezione chirurgica, lo stato dei margini di resezione ha notevoli implicazioni da un punto di vista clinico e prognostico. Infatti, il coinvolgimento dei margini di resezione da parte della neoplasia rappresenta invariabilmente un fattore prognostico sfavorevole, oltre che implicare la necessità di intensificare i trattamenti postchirurgici (e.g., ponendo indicazione alla chemioradioterapia adiuvante), comportando una maggiore tossicità per il paziente. La proporzione di resezioni con margini positivi (i.e., coinvolti dalla neoplasia) nel distretto testa-collo è tra le più elevate in ambito di chirurgia oncologica. In tale contesto si pone l’obiettivo del dottorato di cui questa tesi riporta i risultati. Le due tecnologie di cui si è analizzata l’utilità in termini di ottimizzazione dello stato dei margini di resezione sono la navigazione chirurgica con rendering tridimensionale e la realtà aumentata basata sulla videoproiezione di immagini. Le sperimentazioni sono state svolte parzialmente presso l’Università degli Studi di Brescia, parzialmente presso l’Azienda Ospedale Università di Padova e parzialmente presso l’University Health Network (Toronto, Ontario, Canada). I risultati delle sperimentazioni incluse in questo elaborato dimostrano che l'impiego della navigazione chirurgica con rendering tridimensionale nel contesto di procedure oncologiche ablative cervico-cefaliche risulta associata ad un vantaggio significativo in termini di riduzione della frequenza di margini positivi. Al contrario, le tecniche di realtà aumentata basata sulla videoproiezione, nell'ambito della sperimentazione preclinica effettuata, non sono risultate associate a vantaggi sufficienti per poter considerare tale tecnologia per la traslazione clinica.Head and neck malignancies are an heterogeneous group of tumors. Surgery represents the mainstay of treatment for the large majority of head and neck cancers, with ablation being aimed at removing completely the tumor. Radiotherapy and systemic therapy have also a substantial role in the multidisciplinary management of head and neck cancers. The quality of surgical ablation is intimately related to margin status evaluated at a microscopic level. Indeed, margin involvement has a remarkably negative effect on prognosis of patients and mandates the escalation of postoperative treatment by adding concomitant chemotherapy to radiotherapy and accordingly increasing the toxicity of overall treatment. The rate of margin involvement in the head and neck is among the highest in the entire field of surgical oncology. In this context, the present PhD project was aimed at testing the utility of 2 technologies, namely surgical navigation with 3-dimensional rendering and pico projector-based augmented reality, in decreasing the rate of involved margins during oncologic surgical ablations in the craniofacial area. Experiments were performed in the University of Brescia, University of Padua, and University Health Network (Toronto, Ontario, Canada). The research activities completed in the context of this PhD course demonstrated that surgical navigation with 3-dimensional rendering confers a higher quality to oncologic ablations in the head and neck, irrespective of the open or endoscopic surgical technique. The benefits deriving from this implementation come with no relevant drawbacks from a logistical and practical standpoint, nor were major adverse events observed. Thus, implementation of this technology into the standard care is the logical proposed step forward. However, the genuine presence of a prognostic advantage needs longer and larger study to be formally addressed. On the other hand, pico projector-based augmented reality showed no sufficient advantages to encourage translation into the clinical setting. Although observing a clear practical advantage deriving from the projection of osteotomy lines onto the surgical field, no substantial benefits were measured when comparing this technology with surgical navigation with 3-dimensional rendering. Yet recognizing a potential value of this technology from an educational standpoint, the performance displayed in the preclinical setting in terms of surgical margins optimization is not in favor of a clinical translation with this specific aim

Time-efficient sparse analysis of histopathological Whole Slide Images

International audienceHistopathological examination is a powerful method for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of Whole Slide Images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on specific high and generic low resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40X) high power fields. The time processing to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology

Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.European Unions Horizon 2020 Research and Innovation Programme under the Marie Skodowska-Curie grant agreement No. 656886Austrian National Bank "Jubilaeumsfond" Project 162192020-Research and Innovation Framework Programme PHC-11-2015 667211-2Siemens AustriaNovomedGuerbet, FranceNIH/NCI Cancer Center Support Grant P30CA00874

AI-enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist’s review. CAD systems have many common parts such as image pre-processing, tumor feature extraction and data classification that are mostly based on machine learning (ML) techniques. In this review paper, we describe the application of ML-based CAD systems in MRI of the breast covering the detection of diagnostically challenging lesions such as non-mass enhancing (NME) lesions, multiparametric MRI, neo-adjuvant chemotherapy (NAC) and radiomics all applied to NME. Since ML has been widely used in the medical imaging community, we provide an overview about the state-ofthe-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples illustrating: (i) CAD for the detection and diagnosis, (ii) CAD in multi-parametric imaging (iii) CAD in NAC and (iv) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on ANN in MRI of the breast