Zahlreiche therapie- und prognoseorientierte Studien zum Zervixkarzinom haben eine Uberarbeitung der FIGO-Klassifikation erforderlich gemacht. Das mikroinvasive Karzinom wird allein durch die Tiefe der Stromainfiltration definiert, die flachige (horizontale) Ausdehnung ist nunmehr irrelevant. Makroinvasive Karzinome, die auf die Cervix uteri begrenzt sind, werden anhand der Tumorgrose subkategorisiert in: FIGO IB1/T1b1 – zervikale Stromainvasion >5,0 mm und ≤2 cm maximale Tumorgrose, FIGO IB2/T1b2 – Tumorgrose >2 cm und ≤4 cm, FIGO IB3/T1b3 – Tumorgrose >4 cm. Sowohl die pelvinen als auch die paraaortalen Lymphknoten werden als regionare Lymphknoten definiert. Der Befall pelviner Lymphknoten wird als FIGO IIIC1/pN1a und eine Metastasierung in paraaortale Lymphknoten mit und ohne Beteiligung pelviner Lymphknoten als FIGO IIIC2/pN1b klassifiziert. Der Nachweis einer Corpusinfiltration und eine adnexale/ovarielle Metastasierung sind nicht stagingrelevant.

[The 2019 FIGO classification for cervical carcinoma-what's new?]

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.

/pdf/the-use-of-artificial-intelligence-on-segmental-volumes-3n4n9y0sll.pdf

The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

Stage Migration in Cervical Cancer Using the FIGO 2018 Staging System: A Retrospective Survival Analysis Using a Single-Institution Patient Cohort

Praktisch-diagnostische Aspekte uteriner Leiomyosarkome im Kontext der WHO-Klassifikation 2020

This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologically were included, and according to the examination methods, they were divided into A group with conventional multimodal MRI examination and B group with the multimodal MRI examination under the alternating direction algorithm. The diagnostic results of two types of multimodal MRI for early cervical cancer staging were compared with the results of clinicopathological examination to judge the application value in the early diagnosis of cervical cancer. The results showed that in the 6 randomly selected samples of early cervical cancer patients, the peak signal-to-noise ratio (PSNR) and structural similarity image measurement (SSIM) of multimodal MRI images under the alternating direction algorithm were significantly higher than those of conventional multimodal MRI images and the image reconstruction was clearer under this algorithm. By comparing MRI multimodal staging, statistical analysis showed that the staging accuracy of B group was 75%, while that of A group was only 59.38%. For the results of postoperative medical examinations, the examination consistency of B group was better than that of A group, with a statistically significant difference (P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of B group was larger than that of A group; thus, sensitivity was improved and misdiagnosis was reduced significantly. Multimodal MRI under the alternating direction algorithm was superior to conventional multimodal MRI examination in the diagnosis of early cervical cancer, as the lesions were displayed more clearly, which was conducive to the detection rate of small lesions and the staging accuracy. Therefore, it could be used as an ideal MRI method for the assistant diagnosis of cervical cancer staging.

/pdf/diagnosis-of-early-cervical-cancer-with-a-multimodal-1ncwwjyo.pdf

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm

ZusammenfassungDie Empfehlungen der WHO-Klassifikation von 2014, der TNM-Klassifikation von 2017 sowie der der International Collaboration of Cancer Reporting (ICCR) für gynäkologische Tumoren sind nicht für alle Fälle kongruent. Zwischen WHO und TNM gibt es Unterschiede in der Definition des Vaginalkarzinoms. Insbesondere für seltene gynäkologische Tumoren wie das primäre maligne Melanom und Sarkome von Vulva und Scheide sowie undifferenzierte uterine Sarkome (UUS), der PECom (perivaskulärer epitheloidzelliger Tumor) und extraintestinale gynäkologische GIST (gastrointestinale Stromatumoren) gibt es keine separate TNM-Kategorisierung. Bei nichtuterinen serösen Karzinomen sind im TNM die parakolischen Lymphknoten nicht zugeordnet, wohingegen die FIGO alle intrabadominalen Lymphknoten als regionär klassifiziert.AbstractFor some gynecologic malignancies, there are disagreements between the most recent WHO and TNM classifications and the recommendations of the International Collaboration of Cancer Reporting. These discrepancies are addressed and discussed in this paper. The WHO definition for primary vaginal cancer does not match the TNM definition. The paper also discusses and provides TNM classifications for rare gynecologic tumors like primary malignant vulvar melanomas, sarcomas of the vulva, perivascular epithelioid cell tumor (PECom) of the uterus, undifferentiated uterine sarcomas, and extra-intestinal gastrointestinal stromal tumors (GIST), and provides some recommendations for the reporting and categorization of regional lymph nodes in nonuterine serous pelvic cancer.

TNM-Klassifikation gynäkologischer Tumoren: Was bleibt über 2017 hinaus zu tun?

Die Empfehlungen der WHO-Klassifikation von 2014, der TNM-Klassifikation von 2017 sowie der der International Collaboration of Cancer Reporting (ICCR) fur gynakologische Tumoren sind nicht fur alle Falle kongruent. Zwischen WHO und TNM gibt es Unterschiede in der Definition des Vaginalkarzinoms. Insbesondere fur seltene gynakologische Tumoren wie das primare maligne Melanom und Sarkome von Vulva und Scheide sowie undifferenzierte uterine Sarkome (UUS), der PECom (perivaskularer epitheloidzelliger Tumor) und extraintestinale gynakologische GIST (gastrointestinale Stromatumoren) gibt es keine separate TNM-Kategorisierung. Bei nichtuterinen serosen Karzinomen sind im TNM die parakolischen Lymphknoten nicht zugeordnet, wohingegen die FIGO alle intrabadominalen Lymphknoten als regionar klassifiziert.

TNM-Klassifikation gynäkologischer Tumoren

Das undifferenzierte Endometriumkarzinom (ECX) stellt eine epitheliale Neoplasie des Endometriums ohne eindeutige weitere Differenzierung dar. Beim dedifferenzierten ECX kommt noch ein endometriales Karzinom zur Darstellung, das zumeist einem G1‑/G2-endometrioidem Adenokarzinom entspricht. Der undifferenzierte Anteil zeigt immunhistochemisch einen Verlust von PAX8, E‑Cadherin und eine fokale Expression von EMA bzw. CK18 sowie uberwiegend eine erhaltene Expression von INI‑1 (SMARCB1) und BRG‑1 (SMARCA4). Die wesentlichsten Differenzialdiagnosen sind das endometrioide Adenokarzinom G3, Lymphome, neuroendokrine Karzinome, high-grade endometriale Stromasarkome, undifferenzierte uterine Sarkome (UUS), Karzinosarkome sowie Metastasen. Hinsichtlich der Pathogenese werden derzeit Alterationen des SWI/SNF-Chromatin-Remodelling-Komplexes und eine Anhaufung verschiedener Mutationen (SMARCA4, ARID1B, CTNNB1, PPP2R1A und TP53) beschrieben. Der immunhistochemische Verlust von MLH1 und PMS2 ist zumeist durch eine MLH1-Promotormethylierung bedingt. Nur in Einzelfallen finden sich eine Assoziation zum Lynch-Syndrom oder eine POLE-Mutation. Dieser Tumortyp stellt ein High-grade-Endometriumkarzinom dar, der ein entsprechendes operatives Vorgehen benotigt und eine ungunstigere Prognose impliziert. In Fallen mit Verlust der Mismatch-repair-Proteine oder POLE-Mutationen ergibt sich eine immunonkologische Therapieoption mit Checkpoint-Inhibitoren.

Das un- und dedifferenzierte Endometriumkarzinom

For some gynecologic malignancies, there are disagreements between the most recent WHO and TNM classifications and the recommendations of the International Collaboration of Cancer Reporting. These discrepancies are addressed and discussed in this paper. The WHO definition for primary vaginal cancer does not match the TNM definition. The paper also discusses and provides TNM classifications for rare gynecologic tumors like primary malignant vulvar melanomas, sarcomas of the vulva, perivascular epithelioid cell tumor (PECom) of the uterus, undifferentiated uterine sarcomas, and extra-intestinal gastrointestinal stromal tumors (GIST), and provides some recommendations for the reporting and categorization of regional lymph nodes in nonuterine serous pelvic cancer.

[TNM classification of gynecologic malignancies : What remains to be done beyond 2017?]

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

S. Opitz

Author Tools

Chat about Author