Herbert Loong: Throwback to Papers Whose Questions Still Feel Relevant Today
Herbert Loong/X

Herbert Loong: Throwback to Papers Whose Questions Still Feel Relevant Today

Herbert Loong, Advisor at Greater Bay Area International Clinical Trial Institute, shared on LinkedIn:

“Throwback: revisiting past work

I’m going back through some older publications to share with the community, starting with papers whose questions still feel relevant today. First up: our 2020 ESMO Open study on comorbidities and peripheral blood indices in sarcoma.

The question

TNM staging is the backbone of prognostication, but sarcomas are rare and biologically diverse. The role of a patient’s other medical conditions in their outcome was poorly defined, and earlier studies disagreed.

What we did. Using the Hong Kong Hospital Authority’s territory-wide database, we assembled 3,358 patients with bone or soft-tissue sarcoma diagnosed between 2004 and 2018. At the time, it was the largest population-based sarcoma cohort reported.

What we found:
  • Comorbidities were common. More than 20% of bone sarcoma patients and more than 30% of soft-tissue sarcoma patients had at least one at diagnosis. Diabetes was the most frequent at 9.8%, rising to 12.5% by five years.
  • Mortality rose stepwise with the Charlson Comorbidity Index, even after adjusting for age.
  • Two cheap, routine blood ratios carried real prognostic weight. Five-year survival was 66% with NLR <2.5 and 36% with NLR ≥2.5. Patients with both NLR and PLR elevated fared worst.
  • The relationship wasn’t linear. The added risk plateaued at higher NLR and PLR values, which matters for anyone building nomograms.
Why it still matters

Many sarcoma patients are already seeing a doctor for something else. The endocrinologist managing someone’s diabetes may be the first to hear about a rapidly growing lump. We can raise the index of suspicion outside oncology, and once sarcoma is diagnosed, we can manage comorbidities actively.

Limitations

Diagnoses came from coding, and we had no histology-level data. This is a macro view across subtypes.

Grateful to my co-first authors Wong King Ho, Carlos and Yihui Wei and to the whole team.”

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