Preface — Why Hodgkin Lymphoma and Why This Analysis

How the application thinks about cancers (and why it scales)

This page isn’t a definition of Hodgkin lymphoma. It’s an explanation of how I study it—and how that approach turns into something other people can actually use.

I built this system after seeing the same pattern over and over: medical information is often either too generic to be actionable, or so technical that it only works for people already living inside the jargon.

In pharmaceutical and scientific research, I’ve learned that the same underlying truth can be communicated in multiple valid ways. The deciding factor is rarely intelligence; it’s relevance: what a person is trying to do with the information in that moment.

The core idea behind my Hodgkin lymphoma work (and the broader cancer application it feeds) is simple:

People don’t ask “medical questions” in the abstract. They ask questions from roles.

And each role cares about different facets of the disease, at different depths, for different reasons. So the system I built is a matrix, not a monolith:

• One axis is Perspective (who is asking, and what they need)
• The other axis is Facet (what domain of the disease we are talking about)

That matrix lets me generate targeted, evidence-linked outputs—without “blurring upward” (too simplistic for experts) or “blurring downward” (too clinical for patients).

This generalizes beyond HL because it’s a role-based way of organizing any complex, multi-system disease.

The matrix: Perspectives × Facets

Perspectives (6):

1. Layperson
2. Patient
3. Pre-Clinician
4. Primary Care Physician (PCP)
5. Specialist/Oncologist
6. Researcher

Facets (modules of HL knowledge):

• Foundations (biology, what HL is, epidemiology, symptoms, classification)
• Diagnostics (presentation, staging, biopsy/histology, imaging, differential)
• Patient Care (journey, side effects, survivorship, mental health, practical support)
• Treatments (standard regimens, de-escalation, PET-guided approaches, emerging therapies)
• Molecular & Genetics Biology (RS cell biology, pathways, EBV, immune evasion, resistance)
• Research Frontiers & Clinical Trials (novel agents, mechanisms, resistance, MRD, trials)

Not every facet belongs to every perspective. That’s intentional.

• In layperson/patient modes, the emphasis is clarity, orientation, and real-world decision support. Technical depth is used when it meaningfully improves understanding or choices.
• In researcher modes, the emphasis is mechanisms, evidence structure, and uncertainty. Orientation material is kept brief so the focus stays on what’s known, what’s emerging, and what’s unresolved.

The point is alignment: right depth, right lens, right moment.

What the matrix actually looks like

This is the gating logic in plain English: each facet has a minimum perspective where it becomes appropriate. Everything above that tier can still use it; everything below it should not be forced to. After reviewing thousands of journal articles, it becomes clear that the traditional format is increasingly antiquated—often more tedious than supportive of meaningful research. Even within a single field, such as Hodgkin lymphoma, papers tend to repeat the same introductions and methods sections, differing primarily in hypotheses, experimental design, data acquisition, analysis, and conclusions. A significant portion—30–40%—of each article consists of recycled content, with methods and background rarely changing over decades. New techniques emerge, but they are referential; familiarity renders repeated explanations unnecessary. After extensive engagement with this literature, it is possible to focus directly on the novel contributions. The architecture of Trellis Cancer is designed to mirror this efficiency: minimizing redundancy while highlighting actionable, evidence-linked insights.

Facet	Layperson	Patient	Pre-Clinician	PCP	Oncologist	Researcher
Foundations	✓	✓	✓	✓	✓	✓
Diagnostics			✓	✓	✓	✓
Patient Care	✓	✓		✓
Treatments		✓	✓	✓	✓	✓
Molecular & Genetics Biology					✓	✓
Research Frontiers & Clinical Trials					✓	✓

That table is small, but it does something important: it reduces irrelevant detours (e.g., dumping pathway diagrams into a layperson explainer), and it also prevents the opposite failure mode (e.g., expert outputs padded with orientation material).

The six perspectives (what changes, and why)

1 — Layperson

This is the entry point designed for fast orientation.

The layperson perspective prioritizes plain language, with molecular detail added only when it changes the understanding in a useful way. I learned this the hard way in R&D: you can be right and still lose the room.

For example, when presenting complex genetics data to managers in R&D, I could literally watch attention drop the moment I started talking about the importance of a specific codon at a particular position in a sequence and how it was affecting epigenetic conformation. Not because people didn’t care, but because the detail wasn’t connected to the decision in front of them.

So the layperson output is about practical literacy:

• What is HL at a high level?
• What does it look like in the real world?
• What terms do you need to understand the conversation?

2 — Patient

The patient perspective is different, and it matters.

When I was first diagnosed with a SLAP tear, I felt blindsided. I didn’t understand the condition, the diagnostic path, the treatment course, or even what questions I should be asking. I was drifting—hoping my clinical team knew what they were doing—while trying to research my way back into control. This wasn't a failure of the clinical team, but an inability for myself to communicate well with all of them.

At the same time, life didn’t pause. I was juggling a full-time role leading multiple research labs, managing housing at a university during COVID-19, and trying to have some semblance of a life in between appointments and surgeries. The pain was severe enough that I was vomiting multiple times a day while still trying to “show up” like nothing was happening. As a patient, I was unable to navigate what I truly needed to best serve my health. As a scientific researcher, I spent a significant amount of time trying to educate myself on the specifics only to find myself still lost amongst the data and technical details.

That experience shaped the patient lens: it needs to bridge the gap between understanding and action.

• What happens next?
• What questions should I ask?
• What side effects or complications should I anticipate?
• How do I prepare for the course of care and for life continuing around it?

3 — Pre-Clinician

This perspective is for students who want more than a foundation-level overview—pre-med, advanced undergrad, graduate, or anyone moving into clinical reasoning.

This is where mechanisms and systems thinking enter the picture:

• What defines the entity (and what it is not)
• What are the canonical markers and exceptions
• How do pathways and microenvironment dynamics shape the phenotype
• Where is the evidence incomplete

In HL specifically, this includes anatomical localization, phenotype, immunologic context, and the beginnings of molecular and diagnostic reasoning.

This is also where the “frame” changes: a pre-clinician document shouldn’t read like a polished patient handout. It should define boundaries, mark contrasts, and map uncertainty. It’s not “what should I ask my doctor?”—it’s “what defines the entity, what distinguishes it, and what does the literature still fail to explain?”

4 — Primary Care Physician (PCP)

For a long time, I misunderstood what PCPs are optimized for. I expected them to be a one-stop shop for anything medical. Over time (and through my own injuries/conditions), I learned the real value of primary care: they coordinate the long arc.

My PCP wasn’t a surgeon. They weren’t supposed to have a precise surgical plan for a SLAP tear. What they did do was manage symptoms, initiate the right referrals, and package the story correctly so the specialist wasn’t starting from a blank page.

PCPs are not expected to “be the oncologist.” But they are expected to interrupt delay, translate symptoms into actionable next steps, and hold the patient’s narrative across decades.

They also do something that’s easy to underestimate until you need it: they reduce friction in the system. A knowledgeable PCP sends a referral with supporting data—baseline labs, relevant imaging, a coherent timeline—so the specialist can be effective from day one.

For HL, that means thinking longitudinally over 10–40 years:

• Pre-diagnosis: pattern recognition and preventing delay
• During treatment: triage, infection vigilance, coordination
• Early survivorship (0–5 years): fatigue, anxiety, return-to-work/school, monitoring
• Late survivorship (5–30+ years): late effects, cardiometabolic risk, secondary malignancies, preventive care

Many late effects present first in primary care—not oncology. That is a responsibility, not a footnote.

5 — Oncologist

Oncologists are the domain experts. Their workflow is generally direct: corroborate the diagnosis, stage/stratify, and execute a plan with the best chance of success.

In my own world, one of my most instructive specialist experiences was spine-focused. The pattern is consistent: specialists want the right imaging and tests, and they want them early. They validate symptoms, lock in the working model, and then move quickly.

They’re not doing exploratory research for posterity in the clinic; they’re optimizing outcomes for an individual human being under real constraints—efficacy, toxicity, comorbidities, fertility, long-term risk, and quality of life.

In this lens, “expert-level” means:

• decision points
• response adaptation
• toxicity tradeoffs
• sequencing
• and, increasingly, trial-driven frontiers that are changing practice

6 — Researcher

This perspective is less interpersonal and more structural.

A researcher is not advocating for one specific patient in one specific encounter. A researcher is integrating the entire system—cell biology, microenvironment, genetics/epigenetics, clinical phenotypes, population outcomes—and then stretching into what is not yet known.

Researchers can come off as impersonal. It’s usually not; it’s a commitment to objectivity.

If you ask a researcher about HL, they can talk for hours about regulatory cascades, system interactions, microenvironment dependence, resistance mechanisms, detection strategies, and how population response data corroborates (or challenges) a mechanistic story. That intensity exists because the researcher is mapping the disease toward a resolution—not just a treatment course.

On “cure”: I’m careful with the word. In most settings, “cure” can be interpreted as imprecise.

That’s not because of “pharma doesn’t want cures” narratives. It’s because cancer is heterogeneous: multiple forms, multiple developmental paths, multiple treatment sensitivities, different organs, different populations, different everything. Even within oncology, there are layers of sub-specialty (brain vs liver vs hematologic malignancies, etc.).

But in the researcher lens, the north star is still resolution of the disease in totality—even if the practical path is incremental, and even if the word “cure” has to be used with precision.

Why this matters (and why it’s not HL-only)

What I’m building here is not just educational content. It’s role reinforcement.

Each tier answers a different question:

• Layperson: “What is this, broadly?”
• Patient: “What happens to me, and what do I do next?”
• Pre-Clinician: “What defines this entity, mechanistically?”
• PCP: “How do I prevent delay and carry the long arc of care?”
• Oncologist: “What is decision-grade, evidence-linked, and best for this case?”
• Researcher: “What is established, what is emerging, and what experiments move the field?”

HL is the exemplar because it’s a deep, rich problem with strong clinical outcomes, complex biology, and meaningful survivorship considerations. But the matrix itself is disease-agnostic—and that’s the point: once the architecture is built, it becomes a repeatable way to study and explain other cancers with precision, respect for the audience, and minimal wasted motion.

The end goal for the article isn’t “look how much I know.” It’s to show a method: a structured way to deconstruct a cancer, assemble it into a usable knowledge system, and then translate that knowledge appropriately depending on who is asking and what they’re trying to do.

The mission of this application is to provide a means of reducing friction in knowledge acquisition so that research and understanding can not only accelerate, but be harmonized for improved outcomes.

This dream moves quietly, but its echo can change everything.