Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer.

In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = -0.38, P = .03 and ρ = -0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Single-breath-hold photoacoustic computed tomography of the breast

We have developed a single-breath-hold photoacoustic computed tomography (SBH-PACT) system to reveal detailed angiographic structures in human breasts. SBH-PACT features a deep penetration depth (4 cm in vivo) with high spatial and temporal resolutions (255 µm in-plane resolution and a 10 Hz 2D frame rate). By scanning the entire breast within a single breath hold (~15 s), a volumetric image can be acquired and subsequently reconstructed utilizing 3D back-projection with negligible breathing-induced motion artifacts. SBH-PACT clearly reveals tumors by observing higher blood vessel densities associated with tumors at high spatial resolution, showing early promise for high sensitivity in radiographically dense breasts. In addition to blood vessel imaging, the high imaging speed enables dynamic studies, such as photoacoustic elastography, which identifies tumors by showing less compliance. We imaged breast cancer patients with breast sizes ranging from B cup to DD cup, and skin pigmentations ranging from light to dark. SBH-PACT identified all the tumors without resorting to ionizing radiation or exogenous contrast, posing no health risks

Quantitative Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Breast Images: Optimization of the Time-to-Peak as a Diagnostic Indicator

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in the diagnosis of breast cancer and as an aid in the management of this disease. Although DCE-MRI has a high sensitivity for the detection of malignant breast lesions, distinguishing malignant from benign lesions is more challenging for this method and may depend to some extent on how the images are analysed. Although clinical assessment of these images typically involves qualitative assessment by an expert, there is growing interest in the development of quantitative and automated methods to assist the expert assessment. This thesis involves the quantitative analysis of a particular empirical feature of the time evolution of the DCE-MRI signal known as the time-to-peak ( 7 ^ ) . In particular, this thesis investigates die feasibility of applying measures sensitive to 7 ^ heterogeneity as indicators for malignancy in breast DCE-MRI. Breast lesions in this study were automatically segmented by K-means clustering. Voxel- by-voxel 7\u27peak values were extracted using an empirical model. The / 1th percentile values (p = 10, 20...) of the 7’peak distribution within each lesion, as well as the fractional and absolute hot spot volumes were determined, where hot spot volume refers to the volume of tissue with 7 ^ less than a threshold value. Using the area under the receiver operating characteristic curve (AUC), these measures were tested as indicators for differentiating fibroadenomas from invasive lesions and from ductal carcinoma in situ, as well as for differentiating non-fibroadenoma benign lesions from these malignant lesions. For differentiating fibroadenomas from malignant lesions, low percentile values (p = 10) provided high diagnostic performance. At the optimal threshold (3 min), the hot spot volume provided high diagnostic performance. However, non-fibroadenoma benign lesions were quite difficult to distinguish from malignant lesions. This thesis demonstrates that quantitative analysis of the 7’peak distribution can be optimized for diagnostic performance providing indicators sensitive to intra-lesion r peak heterogeneity

Multimodal Magnetic Resonance and Near-Infrared-Fluorescent Imaging of Intraperitoneal Ovarian Cancer Using a Dual-Mode-Dual-Gadolinium Liposomal Contrast Agent.

The degree of tumor removal at surgery is a major factor in predicting outcome for ovarian cancer. A single multimodality agent that can be used with magnetic resonance (MR) for staging and pre-surgical planning, and with optical imaging to aid surgical removal of tumors, would present a new paradigm for ovarian cancer. We assessed whether a dual-mode, dual-Gadolinium (DM-Dual-Gd-ICG) contrast agent can be used to visualize ovarian tumors in the peritoneal cavity by multimodal MR and near infra-red imaging (NIR). Intraperitoneal ovarian tumors (Hey-A8 or OVCAR3) in mice enhanced on MR two days after intravenous DM-Dual Gd-ICG injection compared to controls (SNR, CNR, p < 0.05, n = 6). As seen on open abdomen and excised tumors views and confirmed by optical radiant efficiency measurement, Hey-A8 or OVCAR3 tumors from animals injected with DM-Dual Gd-ICG had increased fluorescence (p < 0.05, n = 6). This suggests clinical potential to localize ovarian tumors by MR for staging and surgical planning, and, by NIR at surgery for resection

Investigating the role of machine learning and deep learning techniques in medical image segmentation

openThis work originates from the growing interest of the medical imaging community in the application of machine learning techniques and, from deep learning to improve the accuracy of cancerscreening. The thesis is structured into two different tasks. In the first part, magnetic resonance images were analysed in order to support clinical experts in the treatment of patients with brain tumour metastases (BM). The main topic related to this study was to investigate whether BM segmentation may be approached successfully by two supervised ML classifiers belonging to feature-based and deep learning approaches, respectively. SVM and V-Net Convolutional Neural Network model are selected from the literature as representative of the two approaches. The second task related to this thesisis illustrated the development of a deep learning study aimed to process and classify lesions in mammograms with the use of slender neural networks. Mammography has a central role in screening and diagnosis of breast lesions. Deep Convolutional Neural Networks have shown a great potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and reproducibility. A traditional convolution network was compared with a novel one obtained making use of much more efficient depth wise separable convolution layers. As a final goal to integrate the system developed in clinical practice, for both fields studied, all the Medical Imaging and Pattern Recognition algorithmic solutions have been integrated into a MATLAB® software packageopenInformatica e matematica del calcologonella gloriaGonella, Glori

Hyperpolarized 13C-pyruvate MRI detects real-time metabolic flux in prostate cancer metastases to bone and liver: a clinical feasibility study.

BackgroundHyperpolarized (HP) 13C-pyruvate MRI is a stable-isotope molecular imaging modality that provides real-time assessment of the rate of metabolism through glycolytic pathways in human prostate cancer. Heretofore this imaging modality has been successfully utilized in prostate cancer only in localized disease. This pilot clinical study investigated the feasibility and imaging performance of HP 13C-pyruvate MR metabolic imaging in prostate cancer patients with metastases to the bone and/or viscera.MethodsSix patients who had metastatic castration-resistant prostate cancer were recruited. Carbon-13 MR examination were conducted on a clinical 3T MRI following injection of 250 mM hyperpolarized 13C-pyruvate, where pyruvate-to-lactate conversion rate (kPL) was calculated. Paired metastatic tumor biopsy was performed with histopathological and RNA-seq analyses.ResultsWe observed a high rate of glycolytic metabolism in prostate cancer metastases, with a mean kPL value of 0.020 ± 0.006 (s-1) and 0.026 ± 0.000 (s-1) in bone (N = 4) and liver (N = 2) metastases, respectively. Overall, high kPL showed concordance with biopsy-confirmed high-grade prostate cancer including neuroendocrine differentiation in one case. Interval decrease of kPL from 0.026 at baseline to 0.015 (s-1) was observed in a liver metastasis 2 months after the initiation of taxane plus platinum chemotherapy. RNA-seq found higher levels of the lactate dehydrogenase isoform A (Ldha,15.7 ± 0.7) expression relative to the dominant isoform of pyruvate dehydrogenase (Pdha1, 12.8 ± 0.9).ConclusionsHP 13C-pyruvate MRI can detect real-time glycolytic metabolism within prostate cancer metastases, and can measure changes in quantitative kPL values following treatment response at early time points. This first feasibility study supports future clinical studies of HP 13C-pyruvate MRI in the setting of advanced prostate cancer