ME'DA OSTI BEZI O'SMALARI DIFFERENSIAL DIAGNOSTIKASIDA KT-RADIOMIKA VA KLINIK LABORATOR KO'RSATKICHLARNI INTEGRATSIYALASHGAN BAHOLASH

Ushbu tadqiqot me'da osti bezi o'smalarini operatsiyadan oldingi bosqichda tabaqalashtirishda kompyuter tomografiyasi radiomikasi va klinik-laborator markerlar integratsiyasining diagnostik samaradorligini tizimli tahlil qilishga qaratilgan. PubMed, Google Scholar va Scopus bazalaridagi 2020–2026-yillarga oid 45 dan ortiq ilmiy manbalar ko'rib chiqildi. Tahlillar shuni ko'rsatdiki, an'anaviy vizual baholash va alohida olingan CA 19-9 onkomarkeri past spetsifiklikka ega. KT tasvirlaridan olingan tekstura belgilari va klinik-yallig'lanish markerlari (NLR, PLR, CRP) kombinatsiyasi asosidagi mashinali o'qitish modellari differensial diagnostika aniqligini (AUC) 0.88–0.94 gacha oshirgan. KT-radiomika va laborator ko'rsatkichlar integratsiyasi preoperativ bosqichda noinvaziv, aniq tashxis qo'yish va optimal jarrohlik taktikasini tanlash imkonini beruvchi eng samarali multimodal yondashuvdir.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-15
DOI
https://doi.org/10.5281/zenodo.23398506
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

ME'DA OSTI BEZI O'SMALARI DIFFERENSIAL DIAGNOSTIKASIDA KT-RADIOMIKA VA KLINIK LABORATOR KO'RSATKICHLARNI INTEGRATSIYALASHGAN BAHOLASH

Munisa Normurod qizi Ikromova
Zenodo (CERN European Organization for Nuclear Research)
Radiomics and Machine Learning in Medical Imaging
article

ME'DA OSTI BEZI O'SMALARI DIFFERENSIAL DIAGNOSTIKASIDA KT-RADIOMIKA VA KLINIK LABORATOR KO'RSATKICHLARNI INTEGRATSIYALASHGAN BAHOLASH

Munisa Normurod qizi Ikromova
article en

Abstract

Ushbu tadqiqot me'da osti bezi o'smalarini operatsiyadan oldingi bosqichda tabaqalashtirishda kompyuter tomografiyasi radiomikasi va klinik-laborator markerlar integratsiyasining diagnostik samaradorligini tizimli tahlil qilishga qaratilgan. PubMed, Google Scholar va Scopus bazalaridagi 2020–2026-yillarga oid 45 dan ortiq ilmiy manbalar ko'rib chiqildi. Tahlillar shuni ko'rsatdiki, an'anaviy vizual baholash va alohida olingan CA 19-9 onkomarkeri past spetsifiklikka ega. KT tasvirlaridan olingan tekstura belgilari va klinik-yallig'lanish markerlari (NLR, PLR, CRP) kombinatsiyasi asosidagi mashinali o'qitish modellari differensial diagnostika aniqligini (AUC) 0.88–0.94 gacha oshirgan. KT-radiomika va laborator ko'rsatkichlar integratsiyasi preoperativ bosqichda noinvaziv, aniq tashxis qo'yish va optimal jarrohlik taktikasini tanlash imkonini beruvchi eng samarali multimodal yondashuvdir.

Zenodo (CERN European Organization for Nuclear Research)
Good health and well-being
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
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