Radiomic Features from Post-Operative 18F-FDG PET/CT and CT Imaging Associated with Locally Recurrent Rectal Cancer: Preliminary Findings

Locally Recurrent Rectal Cancer (LRRC) remains a major clinical concern, it rapidly invades pelvic organs and nerve roots, causing severe symptoms. Curative-intent salvage therapy offers the only potential for cure but it has a higher chance of success when LRRC is diagnosed at an early stage. Imaging diagnosis of LRRC is very challenging due to fibrosis and inflammatory pelvic tissue which can mislead even the most expert reader. This study exploited a radiomic analysis to enrich, through quantitative features, the characterization of tissue properties, thus favouring an accurate detection of LRRC by Computed Tomography (CT) and 18F-FDG-Positron Emission Tomography/CT (PET/CT). Of 563 eligible patients, undergoing radical resection (R0) of primary RC, 57 patients with suspected LRRC were included, 33 of which histologically confirmed. After manually segmenting suspected LRRC in CT and PET/CT, 144 radiomic features (RFs) were generated, and RFs were investigated for univariate significant discriminations (Wilcoxon rank-sum test, p<0.050) of LRRC from NO LRRC. Five RFs in PET/CT (p<0.017) and 2 in CT (p<0.022) enabled, individually, a clear distinction of the groups, and one RF was shared by PET/CT and CT. Besides confirming the potential role of radiomics to advance LRRC diagnosis, the aforementioned shared RF describes LRRC as tissues having high local inhomogeneity due to evolving tissue’s properties

Real-time virtual sonography in gynecology & obstetrics. literature's analysis and case series

Fusion Imaging is a latest generation diagnostic technique, designed to combine ultrasonography with a second-tier technique such as magnetic resonance imaging and computer tomography. It has been mainly used until now in urology and hepatology. Concerning gynecology and obstetrics, the studies mostly focus on the diagnosis of prenatal disease, benign pathology and cervical cancer. We provided a systematic review of the literature with the latest publications regarding the role of Fusion technology in gynecological and obstetrics fields and we also described a case series of six emblematic patients enrolled from Gynecology Department of Sant ‘Andrea Hospital, “la Sapienza”, Rome, evaluated with Esaote Virtual Navigator equipment. We consider that Fusion Imaging could add values at the diagnosis of various gynecological and obstetrics conditions, but further studies are needed to better define and improve the role of this fascinating diagnostic tool

MR Imaging Texture Analysis in the Abdomen and Pelvis

Texture analysis (TA) is a form of radiomics and refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MRI of the abdomen and pelvis, with the main strength being quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MR texture analysis (MRTA) including a dependency on image acquisition and reconstruction parameters, non-standardized approaches without or with image filtration, diverse software methods and applications, and statistical challenges relating numerous texture analysis results to clinical outcomes in retrospective pilot studies with small sample sizes. Despite these limitations, there is a growing body of literature supporting MRTA. In this review, the application of MRTA to the abdomen and pelvis will be discussed, including tissue or tumor characterization and response evaluation or prediction of outcomes in various tumors

Reference tissue normalization of prostate MRI with automatic multi-organ deep learning pelvis segmentation

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2018Prostate cancer is the most common cancer among male patients and second leading cause of death from cancer in men (excluding non-melanoma skin cancer). Magnetic Resonance Imaging (MRI) is currently becoming the modality of choice for clinical staging of localized prostate cancer. However, MRI lacks intensity quantification which hinders its diagnostic ability. The overall aim of this dissertation is to automate a novel normalization method that can potentially quantify general MR intensities, thus improving the diagnostic ability of MRI. Two Prostate multi-parametric MRI cohorts, of 2012 and 2016, were used in this retrospective study. To improve the diagnostic ability of T2-Weighted MRI, a novel multi-reference tissue normalization method was tested and automated. This method consists of computing the average intensity of the reference tissues and the corresponding normalized reference values to define a look-up-table through interpolation. Since the method requires delineation of multiple reference tissues, an MRI-specific Deep Learning model, Aniso-3DUNET, was trained on manual segmentations and tested to automate this segmentation step. The output of the Deep Learning model, that consisted of automatic segmentations, was validated and used in an automatic normalization approach. The effect of the manual and automatic normalization approaches on diagnostic accuracy of T2-weighted intensities was determined with Receiver Operating Characteristic (ROC) analyses. The Areas Under the Curve (AUC) were compared. The automatic segmentation of multiple reference-tissues was validated with an average DICE score higher than 0.8 in the test phase. Thereafter, the method developed demonstrated that the normalized intensities lead to an improved diagnostic accuracy over raw intensities using the manual approach, with an AUC going from 0.54 (raw) to 0.68 (normalized), and automatic approach, with an AUC going from 0.68 to 0.73. This study demonstrates that multi-reference tissue normalization improves quantification of T2-weighted images and diagnostic accuracy, possibly leading to a decrease in radiologist’s interpretation variability. It is also possible to conclude that this novel T2-weighted MRI normalization method can be automatized, becoming clinically applicable

Studies on Category Prediction of Ovarian Cancers Based on Magnetic Resonance Images

Ovarian cancer is the gynecological malignant tumor with low early diagnosis rate and high mortality. Ovarian epithelial cancer (OEC) is the most common subtype of ovarian cancer. Pathologically, OEC is divided into two subtypes: Type I and Type II. These two subtypes of OEC have different biological characteristics and treatment response. Therefore, it is important to accurately categorize these two groups of patients and provide the reference for clinicians in designing treatment plans. In the current magnetic resonance (MR) examination, the diagnoses given by the radiologists are largely based on individual judgment and not sufficiently accurate. Because of the low accuracy of the results and the risk of suffering Type II OEC, most patients will undertake the fine-needle aspiration, which may cause harm to patients’ bodies. Therefore, there is need for the method for OEC subtype classification based on MR images. This thesis proposes the automatic diagnosis system of ovarian cancer based on the combination of deep learning and radiomics. The method utilizes four common useful sequences for ovarian cancer diagnosis: sagittal fat-suppressed T2WI (Sag-fs-T2WI), coronal T2WI (Cor-T2WI), axial T1WI (Axi-T1WI), and apparent diffusion coefficient map (ADC) to establish a multi-sequence diagnostic model. The system starts with the segmentation of the ovarian tumors, and then obtains the radiomic features from lesion parts together with the network features. Selected Features are used to build model to predict the malignancy of ovarian cancers, the subtype of OEC and the survival condition. Bi-atten-ResUnet is proposed in this thesis as the segmentation model. The network is established on the basis of U-Net with adopting Residual block and non-local attention module. It preserves the classic encoder/decoder architecture in the U-Net network. The encoder part is reconstructed by the pretrained ResNet to make use of transfer learning knowledge, and bi-non-local attention modules are added to the decoder part on each level. The application of these techniques enhances the network’s performance in segmentation tasks. The model achieves 0.918, 0.905, 0.831, and 0.820 Dice coefficient respectively in segmenting on four MR sequences. After the segmentation work, the thesis proposes a diagnostic model with three steps: quantitative description feature extraction, feature selection, and establishment of prediction models. First, radiomic features and network features are obtained. Then iterative sparse representation (ISR) method is adopted as the feature selection to reduce the redundancy and correlation. The selected features are used to establish a predictive model, and support vector machine (SVM) is used as the classifier. The model achieves an AUC of 0.967 in distinguishing between benign and malignant ovarian tumors. For discriminating Type I and Type II OEC, the model yields an AUC of 0.823. In the survival prediction, patients categorized in high risk group are more likely to have poor prognosis with hazard ratio 4.169

Cancro da Próstata: O Papel da Ressonância Magnética Multiparamétrica

Multiparametric Magnetic Resonance Imaging has been increasingly used for detection, localization and staging of prostate cancer over the last years. It combines high-resolution T2 Weighted-Imaging and at least two functional techniques, which include Dynamic Contrast–Enhanced Magnetic Resonance Imaging, Diffusion-Weighted Imaging, and Magnetic Resonance Imaging Spectroscopy. Although the combined use of a pelvic phased-array and an Endorectal Coil is considered the state-of-the-art for Magnetic Resonance Imaging evaluation of prostate cancer, Endorectal Coil is only absolute mandatory for Magnetic Resonance Imaging Spectroscopy at 1.5 T. Sensitivity and specificity levels in cancer detection and localization have been improving with functional technique implementation, compared to T2 Weighted-Imaging alone. It has been particularly useful to evaluate patients with abnormal PSA and negative biopsy. Moreover, the information added by the functional techniques may correlate to cancer aggressiveness and therefore be useful to select patients for focal radiotherapy, prostate sparing surgery, focal ablative therapy and active surveillance. However, more studies are needed to compare the functional techniques and understand the advantages and disadvantages of each one. This article reviews the basic principles of prostatic mp-Magnetic Resonance Imaging, emphasizing its role on detection, staging and active surveillance of prostate cancer

Diseases of the Abdomen and Pelvis 2018-2021: Diagnostic Imaging - IDKD Book

Gastrointestinal disease; PET/CT; Radiology; X-ray; IDKD; Davo

Intensity modulated radiation therapy and arc therapy: validation and evolution as applied to tumours of the head and neck, abdominal and pelvic regions

Intensiteitsgemoduleerde radiotherapie (IMRT) laat een betere controle over de dosisdistributie (DD) toe dan meer conventionele bestralingstechnieken. Zo is het met IMRT mogelijk om concave DDs te bereiken en om de risico-organen conformeel uit te sparen. IMRT werd in het UZG klinisch toegepast voor een hele waaier van tumorlocalisaties. De toepassing van IMRT voor de bestraling van hoofd- en halstumoren (HHT) vormt het onderwerp van het eerste deel van deze thesis. De planningsstrategie voor herbestralingen en bestraling van HHT, uitgaande van de keel en de mondholte wordt beschreven, evenals de eerste klinische resultaten hiervan. IMRT voor tumoren van de neus(bij)holten leidt tot minstens even goede lokale controle (LC) en overleving als conventionele bestralingstechnieken, en dit zonder stralingsgeïnduceerde blindheid. IMRT leidt dus tot een gunstiger toxiciteitprofiel maar heeft nog geen bewijs kunnen leveren van een gunstig effect op LC of overleving. De meeste hervallen van HHT worden gezien in het gebied dat tot een hoge dosis bestraald werd, wat erop wijst dat deze “hoge dosis” niet volstaat om alle clonogene tumorcellen uit te schakelen. We startten een studie op, om de mogelijkheid van dosisescalatie op geleide van biologische beeldvorming uit te testen. Naast de toepassing en klinische validatie van IMRT bestond het werk in het kader van deze thesis ook uit de ontwikkeling en het klinisch opstarten van intensiteitgemoduleerde arc therapie (IMAT). IMAT is een rotationele vorm van IMRT (d.w.z. de gantry draait rond tijdens de bestraling), waarbij de modulatie van de intensiteit bereikt wordt door overlappende arcs. IMAT heeft enkele duidelijke voordelen ten opzichte van IMRT in bepaalde situaties. Als het doelvolume concaaf rond een risico-orgaan ligt met een grote diameter, biedt IMAT eigenlijk een oneindig aantal bundelrichtingen aan. Een planningsstrategie voor IMAT werd ontwikkeld, en type-oplossingen voor totaal abdominale bestraling en rectumbestraling werden onderzocht en klinisch toegepast

Diffusion-weighted Imaging of Lymph Node Tissue

Purpose: The study investigates the hypothesis of clinically observed decreased apparent diffusion coefficient (ADC) of cancerous lymph nodes can be attributed to increased cellularity. The study characterises the mean diffusivity (MD) of lymph node sub-structures and investigates correlation between MD and cellularity metrics. The study also investigates the theoretical information content of single and multi-biophysical models. Methods:. A 3 mm diameter core sample was extracted from a formalin fixed lymph node tissue post-surgery and imaged using 9.4T and 16.4T Bruker MRI system. Samples were sectioned and stained with haematoxylin and eosin (H&E). Diffusion tensor model was fitted voxelwise and MD values were computed using Matlab. Cellularity metrics includes measurement of nuclear count and nuclear area. Eleven models with combinations of isotropic, anisotropic, and restricted components were tested for diffusion modelling and ranked using the Akaike information criterion (AIC). Results: The findings showed distinct diffusivities of lymph node sub-structures (capsule and parenchyma). Parenchyma in normal lymph node tissues had higher MD (0.71 ± 0.17 µm2/ms) than metastatic parenchyma (0.52 ± 0.08 µm2/ms) and lymphoma (0.47 ± 0.19 µm2/ms). No correlation were observed between MD and nuclear count (r = 0.368) and nuclear area (r = 0.368) respectively at 95 % confidence intervals. The single biophysical models (ADC and DTI) were ranked lowest by AIC. Multi-biophysical models consist of anisotropic and restricted diffusion (Zeppelin-sphere, Ball-stick-sphere, and Ball-sphere) were ranked highest in the majority of voxels of the tissue samples. Conclusion: A distinct diffusivity value were found in lymph node sub-structures with no correlation to cellularity. Multi-biophysical models were ranked highest and extract more information from the measurement data than simple single biophysical models