# Assignment3 (BAU, AIN3002, Spring23) — Data

来源：https://www.kaggle.com/competitions/bau-ain3002-spring23-a3/data

采集时间：2026-10-20T05:31:07Z

The dataset consists of pathology reports and corresponding Fuhrman grades of renal cell carcinoma patients in The Cancer Genome Atlas. The dataset is modified from the dataset provided in [1] by Hakan Kıymaz (graduate student, BAU). The labels (Fuhrman grades: G1, G2, G3, or G4) for the training set samples are provided.

## Example from the dataset

**Pathology report text:**
```
procedure: right radical nephrectomy tumor type: papillary renal cell carcinoma nuclear grade: grade iii tumor size: 12 x 11.5 x 9 cm renal vein invasion: not present lymph nodes: not examined phthologic stage: t2 nx mx
```

**Fuhrman grade:**
```
G3
```

The file ``data.tsv`` provides the list of patients and corresponding pathology reports. The file ``train.csv`` provides the list of patients and corresponding Fuhrman grades as one-hot vectors in the training set. The file ``test.csv`` provides the list of patients in the test set.

## Files

* **data.tsv** - all pathology reports
     * *pateint_id* - patient id
     * *pathology_report_text* - unstructured text inside a pathology report
* **train.csv** - the training set
     * *patient_id* - patient id
     * *G1* - the value of class G1 bit in the one-hot vector
     * *G2* - the value of class G2 bit in the one-hot vector
     * *G3* - the value of class G3 bit in the one-hot vector
     * *G4* - the value of class G4 bit in the one-hot vector
*   **test.csv** - the test set
     * *patient_id* - patient id
*   **sample_submission.csv** - a sample submission file in the correct format
     * *patient_id* - patient id
     * *likelihood_G1* - a sample's predicted likelihood of belonging to the class G1
     * *likelihood_G2* - a sample's predicted likelihood of belonging to the class G2
     * *likelihood_G3* - a sample's predicted likelihood of belonging to the class G3
     * *likelihood_G4* - a sample's predicted likelihood of belonging to the class G4

[1] Wu, J., Tang, K., Zhang, H., Wang, C., & Li, C. (2020, December). Structured information extraction of pathology reports with attention-based graph convolutional network. In *2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)* (pp. 2395-2402). IEEE.