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A clinically validated AI framework for kidney cancer detection and characterization – Communications Medicine

A clinically validated AI framework for kidney cancer detection and characterization – Communications Medicine

By Petryshak, Bohdan, Iljin, Mikhail, Denissova, Alina, Ariva, Joonas, Häide, Toomas, Lasn, Veljo, Reim, Martin, Ivaniv, Ihor, Lillsaar, Toomas, Vilt, Hardi, Leht, Anu, Karask, Anti, Kiudma, Gitana, Käpp, Rait, Salumaa, Priit, Ilves, Pilvi & Fishman, Dmytro | 27 November 2025 | Articles, Around the Web
Petryshak et al developed and validated BMVision, an artificial intelligence tool designed to assist radiologists in diagnosing kidney cancer from CT scans. In a reader study with six radiologists, the tool reduces reporting time by 32%, improves detection of benign lesions, and increases ...
Imaging features of fumarate hydratase-deficient renal cell carcinomas: a retrospective study

Imaging features of fumarate hydratase-deficient renal cell carcinomas: a retrospective study

By Ebisu, Naoya, Ueno, Yoshiko, Murakami, Takamichi, Hyodo, Toshiki, Shiraishi, Kaori, Nagayama, Yasunori, Higaki, Atsushi, Tamada, Tsutomu, Fujii, Mikiya, Fukui, Kenichi, Kaga, Tesuro, Matsuo, Masayuki, Takahashi, Satoru & Jinzaki, Masahiro | 27 November 2025 | Articles, Around the Web
Purpose Fumarate hydratase (FH)-deficient renal cell carcinoma (RCC) is a rare and aggressive RCC subtype defined in the 2022 WHO classification. This study aimed to describe its imaging and clinicopathological features. Methods A retrospective analysis was conducted on 11 patients (12 ...
A transcriptional metastatic signature predicts survival in clear cell renal cell carcinoma

A transcriptional metastatic signature predicts survival in clear cell renal cell carcinoma

By Jikuya, Ryosuke, Johnson, Todd A., Muraoka, Erika, Noguchi, Go, Maekawa, Shigekatsu, Obara, Wataru, Numakura, Kazuyuki, Habuchi, Tomonori, Maejima, Kazuhiro, Sasagawa, Shota, Kanazashi, Yuki, Lee, Hwajin, Song, Woo Jeung, Sasagawa, Hajime, Mitome, Taku, Ohtake, Shinji, Kawaura, Sachi, Iribe, Yasuhiro, Aomori, Kota, Nagasaka, Hirotaka, Tatenuma, Tomoyuki, Ueno, Daiki, Komeya, Mitsuru, Ito, Hiroki, Ito, Yusuke, Muraoka, Kentaro, Kawahara, Takashi, Furuya, Mitsuko, Kato, Ikuma, Hamanoue, Haruka, Nishiyama, Akira, Tamura, Tomohiko, Baba, Masaya, Suda, Toshio, Kodama, Tatsuhiko, Ogawa, Takehiko, Uemura, Hiroji, Yao, Masahiro, Tsuzuki, Toyonori, Nagashima, Yoji, Miura, Yuji, Kimura, Go, Imoto, Seiya, Momozawa, Yukihide, Fujii, Satoshi, Makiyama, Kazuhide, Hasegawa, Takanori, Shuch, Brian M., Ricketts, Christopher J., Schmidt, Laura S., Linehan, W. Marston, Nakagawa, Hidewaki & Hasumi, Hisashi | 24 November 2025 | Articles, Around the Web
Rare subtypes of renal cancers can be difficult to diagnose. Here, the authors use transcriptional analysis to show utility in aiding histological classification.
A novel hybrid approach for multi stage kidney cancer diagnosis using RCC ProbNet – Scientific Reports

A novel hybrid approach for multi stage kidney cancer diagnosis using RCC ProbNet – Scientific Reports

By Akram, Zaib, Munir, Kashif, Tanveer, Muhammad Usama, Abid, Yawar Abbas, Sheraz, Muhammad, Hassan, Syed Rizwan & Chuah, Teong Chee | 24 November 2025 | Articles, Around the Web
Kidney renal cell carcinoma (RCC) is a highly invasive malignancy, where early-stage diagnosis can significantly reduce treatment complexity and mortality risk. This study aims to enhance diagnostic precision through a novel model, RCC-ProbNet, capable of identifying and classifying RCC across ...
Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework

Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework

By Ma, Changyi, Feng, Bao, Lei, Yan, Yu, Zhaole, Liu, Yu, Cui, Jin, Li, Rong-gang, Huang, Xiufang, Wu, Baiming, Luo, Zhixin & Cui, Enming | 21 November 2025 | Articles, Around the Web
ccRCC is an aggressive, heterogeneous tumor with a poor prognosis. Prognostic assessments need multi-modal data. Radiological images have limits, while pathological images offer micro-level details. Integrating these for ccRCC outcome prediction is important. Our study aimed to develop and ...
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