<p>Just to report back, I found a reasonable data set on Kaggle:
<a href="https://www.kaggle.com/datasets/ukveteran/stage-c-prostate-cancer" rel="nofollow noreferrer">https://www.kaggle.com/datasets/ukveteran/stage-c-prostate-cancer</a></p>
<p><a href="https://i.sstatic.net/pzc007gf.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/pzc007gf.png" alt="enter image description here" /></a></p>
<p>The top two lines of the data set are posted above, where PG is Proliferation Growth,
G2 is cell cycle G2 phase, ploidy - we all know, EET is  Epoxyeicosatrienoic Acids which is a topical biomarker. It is not a huge data set at 146 patients, but it's enough. I have a fair amount of expertise in genAI. Note, this is an image because the original source data is under Kaggle ownership.</p>
<p>In case you are wondering generative AI (gemAI) is in its infancy in the biotech sector, but that is likely to change and the ability of the largest model to have ingested key data sets in their training is of huge interest. That can only be determined empirically. To give some idea, protein generative AI was part of the last Nobel prize for David Baker. Thats how disruptive its going to become.</p>
<p>There are a fair few miRNA studies on prostate cancer, so there's a major research front in this area.</p>
<p>A second dataset is here:
<a href="https://www.kaggle.com/datasets/nikhilvishwanath/prostatecancer" rel="nofollow noreferrer">https://www.kaggle.com/datasets/nikhilvishwanath/prostatecancer</a></p>
<p><a href="https://i.sstatic.net/A2nltbT8.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/A2nltbT8.png" alt="enter image description here" /></a></p>
<ul>
<li>Logarithmic Prostate Weight is weight</li>
<li>LCAVOL typically refers to &quot;Logarithmic Cancer Volume</li>
<li>LBPH often refers to &quot;Lobular Benign Prostatic Hyperplasia</li>
<li>LPSA typically refers to Logarithmic Prostate-Specific Antigen</li>
<li>Locally Confined Prostate cancer (LCP)</li>
<li>Proliferative Growth possibly pgg45 (dunno)</li>
<li>SVI in the context of prostate cancer typically stands for &quot;Seminal Vesicle Invasion</li>
</ul>
<hr />
<p>I cross posted on Biostars later because its a time sensitive request here:
<a href="https://www.biostars.org/p/9605437/" rel="nofollow noreferrer">https://www.biostars.org/p/9605437/</a></p>
<p>There was zero response.</p>

CC BY-SA 4.0
2024-11-16T22:16:17.973Z
2024-11-18T01:05:03.000Z
2024-11-18T01:05:03.000Z
M__
<p>I am seeking data sets for prostate/cervical cancer. I'm investigating how close synthetic data (genAI) can approximate field data.</p>
<p>In the case of prostate cancer I'm looking for:</p>
<div class="s-table-container"><table class="s-table">
<thead>
<tr>
<th>Age</th>
<th>PSA</th>
<th>Gleason</th>
<th>Stage</th>
<th>Treatment</th>
<th>Side Effects</th>
<th>Outcome</th>
</tr>
</thead>
<tbody>
<tr>
<td>65</td>
<td>10.5</td>
<td>7</td>
<td>II</td>
<td>Radiation</td>
<td>Fatigue</td>
<td>Stable</td>
</tr>
</tbody>
</table></div>
<p>I have investigated several <a href="https://cdas.cancer.gov/datasets/plco/20/" rel="nofollow noreferrer">repos</a> for the data and numerous papers for <a href="https://tau.amegroups.org/article/view/24493/23261" rel="nofollow noreferrer">example</a>. The National Cancer Institute (first hyperlink) requires a formal request and these can take a long time and it's not clear whether the resulting data is appropriate. The data set of the paper isn't available but the code is available (I am not sure Elsevier demand it, Nature and PLoS journals do). In theory I can write to the authors and then the editor if that fails. Again this takes a long time but it depends on journal rules</p>
<p>Again, if anyone does have experience with this data set type, it would be really helpful, because even if I have to submit a formal request I can be confident the data will be what is sought.</p>

<database><cancer><data>
Data sets for prostate/cervical cancer
# Data sets for prostate/cervical cancer

(Asked by: username_0 on 2024-11-08)

I am seeking data sets for prostate/cervical cancer. I'm investigating how close synthetic data (genAI) can approximate field data.

In the case of prostate cancer I'm looking for:

| Age | PSA | Gleason | Stage | Treatment | Side Effects | Outcome |
| --- | --- | --- | --- | --- | --- | --- |
| 65 | 10.5 | 7 | II | Radiation | Fatigue | Stable |

I have investigated several [repos](https://cdas.cancer.gov/datasets/plco/20/) for the data and numerous papers for [example](https://tau.amegroups.org/article/view/24493/23261). The National Cancer Institute (first hyperlink) requires a formal request and these can take a long time and it's not clear whether the resulting data is appropriate. The data set of the paper isn't available but the code is available (I am not sure Elsevier demand it, Nature and PLoS journals do). In theory I can write to the authors and then the editor if that fails. Again this takes a long time but it depends on journal rules

Again, if anyone does have experience with this data set type, it would be really helpful, because even if I have to submit a formal request I can be confident the data will be what is sought.

**Tags:** database, cancer, data

---

## 1 answers

### Answer by username_0 on 2024-11-16. Score: 0 | Accepted answer

Just to report back, I found a reasonable data set on Kaggle:
<https://www.kaggle.com/datasets/ukveteran/stage-c-prostate-cancer>

[![enter image description here](https://i.sstatic.net/pzc007gf.png)](https://i.sstatic.net/pzc007gf.png)

The top two lines of the data set are posted above, where PG is Proliferation Growth,
G2 is cell cycle G2 phase, ploidy - we all know, EET is Epoxyeicosatrienoic Acids which is a topical biomarker. It is not a huge data set at 146 patients, but it's enough. I have a fair amount of expertise in genAI. Note, this is an image because the original source data is under Kaggle ownership.

In case you are wondering generative AI (gemAI) is in its infancy in the biotech sector, but that is likely to change and the ability of the largest model to have ingested key data sets in their training is of huge interest. That can only be determined empirically. To give some idea, protein generative AI was part of the last Nobel prize for David Baker. Thats how disruptive its going to become.

There are a fair few miRNA studies on prostate cancer, so there's a major research front in this area.

A second dataset is here:
<https://www.kaggle.com/datasets/nikhilvishwanath/prostatecancer>

[![enter image description here](https://i.sstatic.net/A2nltbT8.png)](https://i.sstatic.net/A2nltbT8.png)

* Logarithmic Prostate Weight is weight
* LCAVOL typically refers to "Logarithmic Cancer Volume
* LBPH often refers to "Lobular Benign Prostatic Hyperplasia
* LPSA typically refers to Logarithmic Prostate-Specific Antigen
* Locally Confined Prostate cancer (LCP)
* Proliferative Growth possibly pgg45 (dunno)
* SVI in the context of prostate cancer typically stands for "Seminal Vesicle Invasion

---

I cross posted on Biostars later because its a time sensitive request here:
<https://www.biostars.org/p/9605437/>

There was zero response.


---

