u/GastroAGI

I’ve been thinking about this a lot after clinic. Almost every NAFLD consult ends the same way. Which is to loss 7–10% weight, cut carbs, exercise, and wait for 6 months

I know that this is evidence-based. If someone actually pulls off 7 to 10% weight loss, NASH improves in a lot of cases, even fibrosis can move a bit.

But one thing is definitely bothering me. How many people actually manage to do that and sustain it? Because I feel it needs genuine efforts & discipline to reduce weight. So it sometimes feels like I am giving the “right” advice on paper, but in reality, I know most patients won’t be able to follow through fully.

For early-stage disease (F0–F1), I still think this approach is totally fine.

But when it’s F2–F3 (especially with obesity or diabetes), we actually have options like Resmetirom and GLP-1s like Semaglutide that are showing solid results.

I’m not saying lifestyle shouldn’t be pushed. But using it as the only plan in someone with progressing fibrosis feels a bit outdated now. And I have also made these mistakes.

Genuinely want to understand what other doctors & practitioners are doing? Are you guys actually starting these meds in practice, or are cost/biopsy requirements still making it tough to use them?

HCC is still deadly mainly because we have been catching it late.

Today morning, I read about "A new Cancer Discovery" study that says that this can be tackled using ML.

What they did was that they built an interpretable model (PRE-Screen-HCC) on 900K+ patients (UK Biobank + All of US), using demographics, lifestyle pointers, data from clinics & labs, and research on genomics & metabolomics.

They mentioned that this ML model outperforms existing risk scores, and works across diverse populations. It also has a usable web calculator which is externally validated.

I feel like this is a shift from statical score to real-world, data-driven risk stratification.

Of course, grass is greener on the one side. I had some doubts. Will this model generalise outside curated datasets? Will clinicians actually use it? How can we combine these predications to the eventual outcomes for patients?