arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.
By Dominik Winter, Dominik Vonficht, Lo\"ic Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, Markus Schick, Ross Stewart, Nicolas Brieu
arXiv:2608. 03145v1 Announce Type: new Abstract: Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured.
By Yesung Cho, Ji Hwan Park, Chanil Kim, Hyewon Kim, Honglan Li, Yumin Lee, Geongyu Lee, Sujeong Hong, Seong Min Park, Yoonyoung Lee, Hee Sool Rho, Sumin Lee, Amos Chungwon Lee, Changhwan Lee, Hwanyoung Shim, Hyunwook Kim, Hyeji Shin, Sanha Park, Jihoon Yu, Yoon Hee Shin, Sooheon Kim, Hyunjin Park, Seung Min Park, Sangwan Kim, Yujung Kim, Sung-Im Do, Eun-Young Kim, Dongmyung Shin, Jongbae Park, In-Gu Do
arXiv:2606. 28676v1 Announce Type: cross Abstract: Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology.
By Sandesh Pokhrel, Hamid Manoochehri, Bodong Zhang, Beatrice S Knudsen, Tolga Tasdizen
arXiv:2607. 18218v1 Announce Type: cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.
By Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao, Hanwen Xu, Jaspreet Bagga, Guanghui Qin, Robert E. Kramer, Cliff Wong, Soohee Lee, Hao Qiu, Theodore Zhengde Zhao, Racheli Ben Shimol, Angela Crabtree, Kevin Matlock, Eduardo Alejandro Lozano Garcia, Naiteek Sangani, Alberto Santamaria-Pang, Jason Entenmann, Alexandra Q. Bartlett, Bill J. Wright, Bernard A. Fox, Brian Piening, Sheng Zhang, Sheng Wang, Tristan Naumann, Carlo Bifulco, Hoifung Poon
The study evaluated nine transcriptomic models—five bulk RNA‑seq and four single‑cell RNA‑seq—designed to predict response to immune checkpoint inhibitors. Across independent datasets, bulk models performed near chance while single‑cell models offered only modest gains, and pathway analyses revealed inconsistent biomarker signals. The results highlight the limited cross‑cohort robustness and biological consistency of current transcriptomic ICI predictors.
By Yuheng Liang, Lucy Chhuo, Ahmadreza Argha, Nona Farbehi, Lu Chen, Roohallah Alizadehsani, Mehdi Hosseinzadeh, Min Yang, Thantrira Porntaveetusm, Youqiong Ye, Hamid Alinejad-Rokny
arXiv:2606.28676v2 Announce Type: replace-cross
Abstract: Predicting distant metastasis from the digital H & E slides of the primary tumor is a critical yet challenging task in computational patholog...
By Sandesh Pokhrel, Hamid Manoochehri, Beatrice S Knudsen, Tolga Tasdizen