Predicting Multiomics Histopathology Features with Surface Parameterizations
Kai Huang , Zhong-Heng Tan , Wenlong Yu , Xiaowei Wang , Yan Ding , Shihui Xu , Zaozao Chen , Yi Zhang , Yun Liu , Wen-Wei Lin , Tiexiang Li , Shing-Tung Yau , Zhongze Gu
Engineering ››
To improve patient stratification and therapeutic response prediction in computational pathology, clinical-grade decision-making has been enhanced by deep learning models, including those used for microsatellite instability (MSI) prediction and molecular subtype classification. However, prior models and training settings have been largely based on the natural image domain, which differs from histopathology data. To mitigate the inductive bias, we introduced surface parameterization, a geometric mapping from the surface to a suitable domain, to transform whole slide images into fixed-sized squares using conformal energy minimization (CEM) and stretch energy minimization (SEM) algorithms. These transformations are tissue-perceptive, enhancing the region of interest (e.g., cancerous areas) to improve model performance. For example, our method improved MSI prediction, achieving an area under the receiver operating characteristic curve (AUROC) of 0.87 for CEM and SEM with a reduced training set, compared with 0.70 for original slides. To validate its clinical applicability, we analyzed consensus molecular subtype (CMS) classification in 17 colorectal cancer (CRC) patients, with concordance rates of 47.1 % (CEM) and 41.2 % (SEM), outperforming the original slides (29.4 %). As a proof-of-concept, we also linked CMS calls to organoid morphology, demonstrating that cystic organoids were more strongly associated with CMS3. This phenotypic feature may be integrated into CMS and improve the evaluation of tissue differentiation. Overall, our method provides new insight into the data processing of computational pathology and demonstrates the performance of state-of-the-art (SOTA) in multiomics prediction.
Surface parameterization / Colorectal cancer / Consensus molecular subtype / Patient-derived organoids
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
|
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
|
| [56] |
|
| [57] |
|
| [58] |
|
| [59] |
|
| [60] |
|
| [61] |
|
| [62] |
|
| [63] |
|
| [64] |
|
/
| 〈 |
|
〉 |
PDF
(4646KB)
