The Role of Nomograms in Treatment Evaluation for Cancer Patients (Podcast)

Optimized models can personalize predictions for patients with prostate, breast and other cancers

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Oncologists traditionally rely on clinical stages, pathologic stages and risk groups for predictions. But nomograms enable clinicians to place everything they know about a patient into one model that is optimized for prediction.

“In oncology, we’re so used to lumping [patients] into buckets based on stage, but we really lose out on the personalization aspect,” says Rahul Tendelkar, MD, a radiation oncologist at Cleveland Clinic Cancer Center. “Patients come in with a new diagnosis and they want to know what are my chances – not what is the larger group’s [chances]. And so these nonograms we use on a regular basis in our clinics try to help patients.”

In a recent episode of Cleveland Clinic’s Cancer Advances podcast, Dr. Tendelkar and Michael Kattan, PhD, chair of the Department of Quantitative Health Sciences at Cleveland Clinic, talk about nomograms for prostate and breast cancer, including:

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  • How clinicians can integrate nomograms into their practice
  • Where quantitative health scientists acquire the necessary data sets
  • The advantages and limitations of nomograms
  • A newly published nomogram for breast cancer
  • What’s on the horizon for prediction models, including the potential for including genomic classifiers

Click the podcast player above to listen to the episode now, or read on for a short edited excerpt. Check out more Cancer Advances episodes at or wherever you get your podcasts.

Excerpt from the podcast

Dr. Kattan: The nomogram term, strictly speaking, applies to the visual on the piece of paper. But it gets morphed into basically a kind of a comprehensive prediction model and equation, which is underneath the hood of the thing. So, you could take the equation and show it on a sheet of paper. You could also make a smartphone app. You could put it directly into your electronic health record system. And so, it just automatically runs.

We’re always looking for ways to make you [clinicians] more efficient. I’m trying to dive into areas where you folks struggle with decisions. Those are the fun ones for me. If you’re in a no brainer situation where you’re going to treat this patient, like so and so, and you don’t really need a prediction to guide you on that, you probably don’t need a nomogram very much for that. But ones where it’s controversial, and you don’t know whether to do A or B and there’s counterarguments and all this stuff – that’s where I like to give you tools to take the prediction part out of it. So that you’re not basically fighting about predictions. You’re more fighting about the outcomes that are at stake and how important they are, and how much the patient cares about them and involving the patient and that, and just get the math off of your plate.