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Ovarian Neoplasm Imaging 1st Editon 2016 Softbound at Meripustak

Ovarian Neoplasm Imaging 1st Editon 2016 Softbound by Luca Saba, U. Rajendra Acharya, Stefano Guerriero, Jasjit S. Suri, Springer

Books from same Author: Luca Saba, U. Rajendra Acharya, Stefano Guerriero, Jasjit S. Suri

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  • General Information  
    Author(s)Luca Saba, U. Rajendra Acharya, Stefano Guerriero, Jasjit S. Suri
    PublisherSpringer
    Edition1st Edition
    ISBN9781489979513
    Pages535
    BindingSoftbound
    LanguageEnglish
    Publish YearSeptember 2016

    Description

    Springer Ovarian Neoplasm Imaging 1st Editon 2016 Softbound by Luca Saba, U. Rajendra Acharya, Stefano Guerriero, Jasjit S. Suri

    ​Diagnostic and pre-operative imaging has become increasingly adopted throughout the field of gynecology. In particular accurate preoperative analysis of ovarian pathology can improve the selection of the correct therapeutical approach, to reduce the length of operations, maximize surgical technique, and can ultimately improve a range of operative outcomes. New imaging modalities have advanced to the point of high resolution, three-dimensional analysis of tissue anatomy, composition and perfusion. Ultrasonography (US), Computed tomographic (CT) and magnetic resonance (MR) imaging have recently emerged as outstanding non-invasive techniques for the detection and characterization of ovarian pathology. In particular, US has probably now imposed itself as the “state-of-the-art” technique to explore the ovarian neoplasm, thanks the technical advancement like texture analysis (MGV) and 3D potentialities, although MR with new sequences like the diffusion-weighted imaging and greater magnetic fields (3 Tesla or more) may become leading methods in the future. The purpose of this book is to cover all the imaging techniques, potential for applying such imaging clinically, and to offer present and future applications as applied to ovarian pathology with the most world renowned scientists in these fields. The book is designed according to the pathological classification of the benign and malignant ovarian neoplasm by presenting for each pathology the clinical setting followed by the imaging approach. At the end of each chapter a “focus concept” paragraph will be presented with take-home point for the readers. Epidemiology.- Histopathology.- Cyst of Follicular origin and Pregnancy Luteoma(CT and MR).-Endometrioma (Clinical Setting & US).- Endometrioma (CT and MR).- Benign Surface.- Epithelial Stromal Tumors (Clinical Setting & US).- Benign Surface Epithelial Stromal Tumors (CT and MR).- Benign Sex Cord - Stromal Tumors (Clinical Setting & US).- BenignSex Cord - Stromal Tumors (CT and MR).- Benign Germ Cell - Stromal Tumors (Clinical Setting & US).- Benign Germ Cell - Stromal Tumors (CT and MR).- Borderline Tumor (Serous\Mucinous\Endometrioid) (Clinical Setting & US).- Borderline Tumor (Serous\Mucinous\Endometrioid) (CT and MR).- Malignant Tumor (Serous\Mucinous\Endometrioid adenocarcinoma) (Clinical Setting & US).- Malignant Tumor (Serous\Mucinous\Endometrioid adenocarcinoma) (CT and MR).- Rare Malignant Tumor (Clear cell adenocarcinoma, transitional cell carcinoma, malignant Brenner Tumor) (Clinical Setting & US).- Rare Tumor (Clear cell adenocarcinoma, transitional cell carcinoma, malignant Brenner Tumor) (CT and MR).- Malignant Sex Cord - Stromal Tumors (Clinical Setting & US).- Malignant Sex Cord - Stromal Tumors (CT and MR).- Malignant Germ Cell - Stromal Tumors (Clinical Setting & US).- Malignant Germ Cell - Stromal Tumors (CT and MR).- Metastatic tumors (Clinical Setting & US).- Metastatic tumors (Serous\Mucinous\Endometrioid) (CT and MR).- 3D Ultrasonography.- Ovarian Tumor Characterization and classification using ultrasound : A new on-line paradigm.- Ovarian Tumor Characterization using 3D ultrasound.- Evolutionary Algorithm based Classifier Parameter Tuning for automatic Ovarian Cancer tissue characteization and Classification.- CT/PET.- Contrast-enhanced transvaginal sonography for early detection of ovarian cancer.- Molecular imaging.- Index.



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