scholarly journals Added Value of SPECT/CT in Characterization of Extra-Skeletal Uptake of Tc-99m MDP on Planar Bone Scintigraphy in Known Cancer Patients.

2021 ◽  
Vol 22 (1) ◽  
pp. 24-36
Author(s):  
Bashank, NM
Keyword(s):  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Added Value

scholarly journals Added value of diffusion-weighted MRI in assessment of pleural lesions

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Youssriah Yahia Sabri ◽  
Ikram Hamed Mahmoud ◽  
Lamis Tarek El-Gendy ◽  
Mohamed Raafat Abd El-Mageed ◽  
Sally Fouad Tadros
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Value ◽  
Added Value ◽  
Adc Value ◽  
Pleural Diseases ◽  
Conventional Mri ◽  
Malignant Lesions

Abstract Background There are many causes of pleural disease including variable benign and malignant etiologies. DWI is a non-enhanced functional MRI technique that allows qualitative and quantitative characterization of tissues based on their water molecules diffusivity. The aim of this study was to evaluate the diagnostic value of DWI-MRI in detection and characterization of pleural diseases and its capability in differentiating benign from malignant pleural lesions. Results Conventional MRI was able to discriminate benign from malignant lesions by using morphological features (contour and thickness) with sensitivity 89.29%, specificity 76%, positive predictive value 89%, negative predictive value 76.92%, and accuracy 85.37%. ADC value as a quantitative parameter of DWI found that ADC values of malignant pleural diseases were significantly lower than that of benign lesions (P < 0.001). Hence, we discovered that using ADC mean value of 1.68 × 10-3 mm2/s as a cutoff value can differentiate malignant from benign pleural diseases with sensitivity 89.3%, specificity 100%, positive predictive value 100%, negative predictive value 81.2%, and accuracy 92.68% (P < 0.001). Conclusion Although DWI-MRI is unable to differentiate between malignant and benign pleural effusion, its combined morphological and functional information provide valid non-invasive method to accurately characterize pleural soft tissue diseases differentiating benign from malignant lesions with higher specificity and accuracy than conventional MRI.

scholarly journals MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2447
Author(s):  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey P. Primakov ◽  
Sanaz Samiei ◽  
Thiemo J. A. van Nijnatten ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Systemic Therapy ◽  
Tumor Response ◽  
Added Value ◽  
Breast Cancer Patients ◽  
Neoadjuvant Systemic Therapy ◽  
Clinical Models ◽  
Combined Models ◽  
Complete Tumor

This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used. Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. Further, the radiomics features selected for the models and their performance differed with and within the different strategies. Due to previous and current work, we tentatively attribute the lack of improvement in clinical models following the addition of radiomics to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data (i.e., test-retest or similar) meant that this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics.

scholarly journals Prospective comparison of the diagnostic accuracy of 18F-FDG PET/MRI, MRI, CT, and bone scintigraphy for the detection of bone metastases in the initial staging of primary breast cancer patients

2021 ◽  
Author(s):  
Nils Martin Bruckmann ◽  
Julian Kirchner ◽  
Lale Umutlu ◽  
Wolfgang Peter Fendler ◽  
Robert Seifert ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastases ◽  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Fdg Pet ◽  
Primary Breast Cancer ◽  
Whole Body ◽  
Newly Diagnosed ◽  
Breast Cancer Patients ◽  
Initial Staging

Abstract Objectives To compare the diagnostic performance of [18F]FDG PET/MRI, MRI, CT, and bone scintigraphy for the detection of bone metastases in the initial staging of primary breast cancer patients. Material and methods A cohort of 154 therapy-naive patients with newly diagnosed, histopathologically proven breast cancer was enrolled in this study prospectively. All patients underwent a whole-body [18F]FDG PET/MRI, computed tomography (CT) scan, and a bone scintigraphy prior to therapy. All datasets were evaluated regarding the presence of bone metastases. McNemar χ2 test was performed to compare sensitivity and specificity between the modalities. Results Forty-one bone metastases were present in 7/154 patients (4.5%). Both [18F]FDG PET/MRI and MRI alone were able to detect all of the patients with histopathologically proven bone metastases (sensitivity 100%; specificity 100%) and did not miss any of the 41 malignant lesions (sensitivity 100%). CT detected 5/7 patients (sensitivity 71.4%; specificity 98.6%) and 23/41 lesions (sensitivity 56.1%). Bone scintigraphy detected only 2/7 patients (sensitivity 28.6%) and 15/41 lesions (sensitivity 36.6%). Furthermore, CT and scintigraphy led to false-positive findings of bone metastases in 2 patients and in 1 patient, respectively. The sensitivity of PET/MRI and MRI alone was significantly better compared with CT (p < 0.01, difference 43.9%) and bone scintigraphy (p < 0.01, difference 63.4%). Conclusion [18F]FDG PET/MRI and MRI are significantly better than CT or bone scintigraphy for the detection of bone metastases in patients with newly diagnosed breast cancer. Both CT and bone scintigraphy show a substantially limited sensitivity in detection of bone metastases. Key Points • [18F]FDG PET/MRI and MRI alone are significantly superior to CT and bone scintigraphy for the detection of bone metastases in patients with newly diagnosed breast cancer. • Radiation-free whole-body MRI might serve as modality of choice in detection of bone metastases in breast cancer patients.

scholarly journals Synthesis and characterization of analcime (ANA) zeolite using a kaolinitic rock

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniela Novembre ◽  
Domingo Gimeno
Keyword(s):  
Ambient Pressure ◽  
Added Value ◽  
Emission Spectrometry ◽  
Quantitative Phase Analysis ◽  
Crystallization Time ◽  
Experimental Protocol ◽  
Economic Point ◽  
Cell Parameters ◽  
Synthesis Protocol

AbstractAnalcime is nowadays an important component in dental porcelain systems, in heterogeneous catalysis, in the nanoelectronic field, in selective adsorption and in stomatology (dental filling and prosthesis). Analcime synthesis from an impure, silica-rich kaolinite rock coming from Romana (Sassari, Italy) is here presented. A synthesis protocol is proposed that aims to make an improvement of synthesis conditions compared to the past. The hydrothermal treatment is in fact here achieved without aging times and without the use of sodium silicate or other additional silica source reported in the literature. Lower calcination temperature, synthesis temperature and crystallization time are verified in this work. The kaolin is subjected to calcination at the temperature of 650 °C and then mixed with NaOH. The experiment is performed at ambient pressure and 170 ± 0.1 °C. The degree of purity of analcime is calculated in 97.57% at 10 h. Analcime is characterized by X-ray diffraction, infrared spectroscopy, scanning electron microscopy, inductively coupled plasma optical emission spectrometry and thermal analysis. Density is also calculated. Cell parameters and the amount of amorphous phase in the synthesis powders is estimated with quantitative phase analysis using the combined Rietveld and reference intensity ratio methods. The experimental conditions make the synthesis protocol particularly attractive from an economic point of view. Also this work does not use a commercial kaolin but silica-rich impure kaolinitic rock from a disused quarry. This further reduces the costs of the experimental protocol. It also gives the protocol an added value, as the synthesis of a useful mineral is obtained through the valorization of an otherwise unused georesource. Both chemical and physical characterization of analcime is satisfactory making the experimental protocol very promising for an industrial transfer.

scholarly journals Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 518
Author(s):  
Da-Chuan Cheng ◽  
Te-Chun Hsieh ◽  
Kuo-Yang Yen ◽  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Deep Learning ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Breast Cancer Patients ◽  
Location Detection ◽  
The Mean ◽  
Mean Sensitivity

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

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