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A nomogram predicting postoperative recurrence risk in SF1/TPIT nonfunctioning pituitary neuroendocrine tumors:
Zhe Zhang1,2, Jie Yin1,3, Mengqing Hu4
11Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing.
Objective:
Nonfunctioning pituitary neuroendocrine tumors (NF-PitNETs) can recur despite gross-total resection (GTR), and the accurate prediction of tumor recurrence remains a major clinical challenge. In this study, authors aimed to evaluate the predictive value of the T2-weighted signal coefficient of variation (T2-CV) and develop an integrated prognostic nomogram for recurrence-free survival (RFS) in NF-PitNET.
Methods:
Tumor heterogeneity was assessed using T2-weighted MRI. Clinical, radiological, and pathological variables were retrospectively collected on patients with a PitNET diagnosis between 2011 and 2024. Kaplan-Meier analysis was used for survival assessment of each parameter. A multivariate Cox proportional hazards model was employed to identify independent predictors of RFS, and a prognostic nomogram was subsequently developed based on these significant factors. The total score derived from the nomogram was used to stratify patients into distinct risk groups.
Results:
This study included 596 patients with NF-PitNET who underwent GTR. Multivariate analysis identified four independent factors associated with shorter RFS: age < 41 years, Knosp grade 3-4, T2-CV ≥ 0.442, and Ki-67 index ≥ 3% (all p < 0.001). A nomogram incorporating these variables was developed and demonstrated superior predictive accuracy (area under the curve [AUC] = 0.801) compared to any single predictor alone (p < 0.001). The nomogram achieved a concordance index of 0.82, demonstrating good calibration and strong clinical applicability. Its time-dependent receiver operating characteristic analysis further demonstrated AUC values of 0.85, 0.89, 0.84, 0.86, and 0.82 at 12, 24, 36, 48, and 60 months, respectively, indicating particular strength in short-term recurrence prediction. Furthermore, applying a cutoff score of 72.41, the nomogram effectively stratified patients into high- and low-risk groups, which were differentiated by significantly different median RFS (81.17 vs 99.93 months, respectively, HR 7.364, 95% CI 4.375-12.393, p < 0.001).
Conclusions:
T2-CV, a novel quantitative MRI parameter, serves as an independent predictor of RFS in patients following GTR of NF-PitNET. A nomogram integrating T2-CV with other key prognostic factors demonstrated high accuracy in identifying high-risk individuals.