ccRCC is the most common type of kidney cancer, accounting for up to 80% of all renal cell carcinoma cases in adults. Estimating the prognosis is critical for patient management, but it is still a very challenging task. Ongoing research on this topic has led to the creation of two public studies: CPTAC-CCRCC and TCGA-KIRC, from which we curated MMIST-ccRCC.
| Patients | CT | MRI | WSI | Genomics | Clinical | |
|---|---|---|---|---|---|---|
| Train | 497 | 189 | 35 | 497 | 361 | 497 |
| Test | 121 | 59 | 13 | 121 | 101 | 121 |
| Total | 618 | 248 | 48 | 618 | 462 | 618 |
Here you can access the metadata with the train/test split (from CPTAC and TCGA repositories).
Below you have the links where you can access the original dataset both from TCGA (XXGB) and CPTAC ('Tissue Slide Images': with 190GB) and the CSV containing the selected files that we used. Be aware that for the TCGA we provide the manifest file that you can use directly with the GDC Data Transfer tool.
Below you can access the CT and MRI images from the same patients both from TCGA ('Images': 91.56GB) and CPTAC repositories ('Radiology Images': 56.58GB), as well as the CSV with the filenames and IDs that we used in our repository.
We discarded the localization scans, the pre-contrast ones, and scans that were acquired with a significant time lapse from the diagnosis (years). We also removed the scans from the coronal and sagittal views, to minimize domain shifts. We reduced the number of volumes to 736 for CT and 552 for MRI.
If you don't want to download the original images, you can use our features.
Here you can access the different folders containing the multi-modal features:
Several patients presented more than one CT/MRI scan and WSI image. We opted to reduce the amount of data used by our multi-modal system to a single CT, MRI, and WSI per patient. We implemented a novel patient-level MIL framework that automatically selects the best imaging modalities from the available pool. Here you can access the different CSV files containing the selected files per patient that were used in our paper:
If you want more information about this dataset feel free to read our paper:
Tiago Mota, Maria Rita Fonseca Verdelho, Diogo José Pereira Araújo, Alceu Bissoto, Carlos Santiago, Catarina Barata, MMIST-ccRCC: A Real-World Medical Dataset for the Development of Multi-Modal Systems, Data Curation and Augmentation in Enhancing Medical Imaging Applications Workshop (archival) @CVPR 2024.
Cite the paper where the dataset was first published and acknowledge us: "The results shown in this work used datasets collected from MMIST: https://Multi-Modal-IST.github.io/"