APPENDICURE

And why submitting your data to the Patient-Led Global Appendix Cancer Registry is one of the most important things you can do right now

If you are living with high-grade appendix cancer, goblet cell adenocarcinoma (GCA), signet ring cell carcinoma, or another aggressive subtype, you already know what the data does not yet show. You know the uncertainty after CRS/HIPEC. You know the limits of CEA and CA 19-9. You know what it feels like to be told that your case is rare and that the research is still catching up.

That last part is what we are trying to change. And the way we change it starts with you.

The research world is moving. Appendix cancer cannot afford to be left behind.

A research group recently published work using a new kind of artificial intelligence model called BINN-FiLM, designed to take complex biological data from patients and do two things that matter enormously for rare cancers: classify disease subtypes more accurately, and identify biomarkers that current tests miss.

In their study, the model outperformed standard machine learning approaches across pancreatic cancer, COVID-19, and a liver cancer dataset. It picked out specific proteins, immune signatures, and metabolic pathways that distinguished disease subtypes from each other. In other words, the technology to find meaningful biological signals in small, complicated patient populations is here.

The bottleneck for appendix cancer is not the science. It is the data.

Models like this need patients. Rare cancers need every single one.

Why high-grade subtypes urgently need their own dataset

High-grade appendiceal cancers do not behave like the low-grade mucinous tumors most of the published literature describes. GCA, signet ring cell carcinoma, and high-grade mucinous adenocarcinoma have their own molecular signatures, their own recurrence patterns, and their own treatment responses. Yet most of what oncologists rely on when treating these patients has been extrapolated from colorectal cancer or from small case series.

That is why patients keep hearing the same things. We are not sure how aggressive your subtype will be. Tumor markers are not reliable for you. There is no standard chemotherapy regimen specifically validated for goblet cell. Recurrence prediction is largely based on clinical experience, not molecular evidence.

None of that changes until we have a dataset large enough to find the patterns. And the only people who can build that dataset are patients.

The questions a registry can finally answer

• Why do some high-grade patients recur within months of HIPEC and others remain disease-free for years?
• Which signet ring patients respond to systemic chemotherapy, and which do not?
• Are there molecular subtypes within GCA that explain why outcomes vary so widely?
• Can we find blood-based markers that actually track disease in patients whose CEA and CA 19-9 stay normal?
• What does long-term survivorship look like, and what predicts it?

These are not abstract research questions. They are the questions patients ask their oncologists every week. Right now, the honest answer is that we do not have enough data to know.

What the AI research actually shows about rare cancers

The BINN-FiLM study is worth understanding because it represents where oncology is heading. The researchers showed that when you feed a biologically informed neural network the right kind of patient data, it can do things that older statistical methods cannot. It can pick out the small handful of proteins among hundreds that distinguish one disease from another. It can map those proteins back to the biological pathways they belong to, so the findings are not just predictive but interpretable. It can do this with relatively modest sample sizes, because the model is structured around biology rather than starting from zero.

For appendix cancer, that last point is the one to hold onto. Traditional AI struggles with rare diseases because there is not enough data to learn from. Biologically informed models change the math. They can extract meaning from smaller datasets, which is exactly what rare cancer research needs.

But the model still needs a dataset to work on. And right now, for appendix cancer, that dataset does not exist at the scale required.

What researchers actually need Diagnosis details, including subtype and grade. This is the single most important field for high-grade research.Pathology specifics like PCI score, signet ring percentage, and lymph node involvement when known.Treatment history. CRS/HIPEC details, systemic chemotherapy regimens, response to treatment.Outcomes over time. Recurrence, surveillance findings, what worked and what did not.Demographic and clinical context that helps researchers understand patterns across the full patient population.

Why one week and 30 some patients is a beginning, not a problem

The Patient-Led Global Appendix Cancer Registry launched recently. So far, 30 some patients have submitted their data. That is a starting point that researchers can already begin to learn from, and it is the foundation we build on. But appendix cancer needs hundreds of patients in this registry to give scientists the statistical power to find real molecular and clinical patterns. For high-grade subtypes specifically, every additional patient meaningfully changes what we can see.

This is the part most patients do not realize. In a common cancer like breast or colorectal, one patient is one data point in a sea of millions. In appendix cancer, your case is a substantial percentage of what the world knows about your subtype. When a GCA patient submits their data, they are not adding to a dataset. They are helping create one.

In rare disease, every patient counts. That is not a slogan. It is statistics.

What submitting your data actually does

The registry is patient-led, which means it was built around what patients can realistically provide rather than what is most convenient for institutions. You do not need to track down old pathology reports before you start. You do not need to know every detail of your treatment history off the top of your head. You can submit what you know now and add more later.

When enough patients have contributed, the registry becomes the resource that researchers, clinicians, and trial designers use to understand the disease. It becomes the dataset that AI tools like the one described in the BINN-FiLM paper can actually work with. It becomes the evidence base that makes the case for new clinical trials, new biomarker studies, and new funding for high-grade subtype research.

Without it, the science stays where it has been: small case series, scattered institutional data, and treatment decisions made on incomplete information.

If you have high-grade appendix cancer, this is the ask

Submit your data. Whether you have GCA, signet ring cell carcinoma, high-grade mucinous adenocarcinoma, or another aggressive subtype, your case is one of the few that researchers can learn from. The form takes about 5 to 10 minutes, and you can skip anything you do not know or want to come back to later.

If you have already had CRS/HIPEC, your data is especially valuable, because long-term outcomes after HIPEC for high-grade subtypes are still poorly understood. If you are newly diagnosed, your data helps establish the baseline researchers need to track disease progression and response to treatment over time. If you are years out from treatment, your data answers questions about long-term survivorship that no one has been able to answer before.

And if you know other patients in our community, share the registry with them. The hardest part of building a dataset for a rare cancer is reaching the patients who exist but have never been counted.

Add your data to the Patient-Led Global Appendix Cancer Registry Visit appendicure.com and follow the link to the registry. The form takes 5 to 10 minutes. You can skip anything you don’t know and come back to it later. You can update your information over time.If you have high-grade appendix cancer, GCA, or signet ring cell carcinoma, we especially need your data. Your subtype is where the research gap is largest, and where new patient contributions have the most impact. HERE is a direct link to the Appendicure Registry.

The bigger picture

The technology to transform appendix cancer research already exists. The clinical interest from major centers is growing. The AI tools that can find biomarkers in rare cancer datasets are being published right now in major journals.

What is missing is the dataset. That is the one piece that cannot be built by researchers alone, no matter how brilliant or well-funded they are. It can only be built by patients, one submission at a time.

If you are a high-grade appendix cancer patient, you are not just a patient. You are part of the small group of people in the world who can change what is known about this disease. Thirty-some patients have already started. Several hundred more would be transformative.

Your case is one of the few that can move this field forward. Please be counted. Register your data HERE! If you need help please reach out to info@appendicure.com.