An article published Online First and in the November issue of The Lancet Oncology reports that a new computerised tool or nomogram for predicting a patient’s risk of cancer recurrence after surgery to remove primary gastrointestinal stromal tumours (GIST) is more precise than current predictive models.
Since cancer is likely to recur, it is important that patients be informed about their probable outcome. Patients should be selected for postoperative treatment if effective additional therapy is available. Even after an apparently successful surgery, cancer recurrence in patients with GIST is frequent. The drug imatinib has recently been approved for the additional treatment of operable GIST. It has been shown to prolong recurrence-free survival (RFS). However, the financial cost and potential toxic effects of imatinib make the capacity to estimate the risk of recurrence in individual patients crucial.
In order to find out more, Ronald DeMatteo from Memorial Sloan-Kettering Cancer Center, USA, and colleagues developed a nomogram. They used three established prognostic criteria: tumour size, location (stomach, small intestine, colon/rectum, or other), and mitotic index. They gathered data from 127 patients with primary GIST to assess RFS after surgery. The nomogram’s mechanism is the totalling of the risk scores linked to each criterion and predicting the likelihood of RFS at two and five years.
Tests to evaluate the nomogram´s precision involved 212 patients with GIST from the Spanish Sarcoma Research Group (GEIS) and 148 patients who had surgery for GIST from The Mayo Clinic. Researchers subsequently compared the predictive ability of the nomogram to three commonly used models or staging systems: US National Institutes of Health (NIH)-Fletcher, NIH-Miettinen, and the recently updated Armed Forces Institute of Pathology (AFIP)-Miettinen.
Generally, results indicated the nomogram was better at predicting the likelihood of RFS than the NIH and AFIP staging systems.
After measuring the concordance probability of the nomogram, the predictive accuracy was 0.78 in the original dataset (78 percent of the time the nomogram accurately predicted the ordering of the outcome between two randomly selected patients), 0.76 in the GEIS, and 0.80 in the Mayo Clinic validation datasets.
Furthermore, concordance probabilities of the nomogram were significantly better than both NIH models when tested on patients in the GEIS cohort (0.76 compared to 0.70 and 0.66) and in the Mayo cohort (0.8 compared to 0.74 and 0.78). In addition, the nomogram had higher but not statistically different concordance probability to that of the AFIP model when tested on patients in both the GEIS (0.76 compared to 0.73) and Mayo cohorts (0.80 compared to 0.76). Supplementary calculations revealed that the nomogram predictions of RFS were better calibrated than predictions made with the AFIP model.
The authors explain: “Overall, prognostic nomograms give better prediction of the likelihood of events for individual patients than do staging systems that stratify patients into a few broad groups.”
They write in conclusion: “The appeal of the current nomogram is that…the variables of tumour size, location, and mitotic index are routinely reported by many pathologists and, therefore, the nomogram should be broadly applicable…The nomogram might be useful for patient care, interpretation of clinical trial results, and the selection of patients for adjuvant imatinib therapy.”
In a supplementary note, Heikki Joensuu from Helsinki University Central Hospital in Finland is pleased with the new tool and refers to it as “a step forward in the individualisation of prognostication.”
“Development and validation of a prognostic nomogram for recurrence-free survival after complete surgical resection of localised primary gastrointestinal stromal tumour: a retrospective analysis”
Jason S Gold, Mithat Gönen, Antonio Gutiérrez, Javier Martín Broto, Xavier García-del-Muro, Thomas C Smyrk, Robert G Maki, Samuel Singer, Murray F Brennan, Cristina R Antonescu, John H Donohue, Ronald P DeMatteo
DOI: 10.1016/S1470-2045(09)70242-6
Written by Stephanie Brunner (B.A.)