Evidence map›Paper›PMID 42509491›Full record

ArticlePituitary2026

Perioperative predictors of biochemical remission in acromegaly: a predictive modeling study.

Hanyao Sun, Biren Patel, Samir Lohana, Rajashree Sarkar, Mario Chiapponi, Ruggero Antonini, Leonardo Tariciotti, J Manuel Revuelta Barbero, Nyrene Haque, Edoardo Porto and 4 more

Abstract read
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In one paragraph

Article in Pituitary, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Hanyao Sun *Department of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Biren Patel *Department of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Samir LohanaDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Rajashree SarkarDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Mario ChiapponiDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Ruggero AntoniniDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Leonardo TariciottiDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
J Manuel Revuelta BarberoDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Nyrene HaqueDepartment of Endocrinology, Emory University, Atlanta, GA, USA.
Edoardo PortoDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
Alejandra RodasDepartment of Otolaryngology, Emory University, Atlanta, GA, USA.
Tomas Garzon-MuvdiDepartment of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA.
C Arturo SolaresDepartment of Otolaryngology, Emory University, Atlanta, GA, USA.
Gustavo Pradilla *Department of Neurosurgery, Emory University, 1365 Clifton Rd. NE, Suite B6200, Atlanta, GA, 30322, USA. gpradil@emory.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBiochemical remission after transsphenoidal resection for acromegaly remains challenging, and early prognostication is limited. This study applies statistical and predictive modeling approaches to identify perioperative predictors of remission.

methodsWe retrospectively analyzed 86 patients with acromegaly who underwent endoscopic endonasal transsphenoidal resection of GH-secreting primary pituitary adenomas between January 2018 and January 2025. Preoperative demographic, biochemical, radiologic, and histopathologic variables were evaluated. Patients were randomly split into training and test sets. Missing data were addressed using median and KNN imputation. Five machine learning models (GBM, RF, GLMNET, KNN, and Nnet) were trained using exhaustive feature subset selection and evaluated using AUROC and accuracy.

resultsOf 86 patients, 62 (72.1%) achieved biochemical remission. Remission was associated with older age (p = 0.016), round tumor shape (p < 0.0001), gross total resection (p < 0.0001), lower Knosp grade (p < 0.0001), and dense CAM 5.2 staining (p = 0.037). The best-performing model was RF using four features (gender, tumor shape, extent of resection, CAM 5.2), achieving an accuracy of 0.8235 and AUROC of 0.8542 on the test set.

conclusionPredictive modeling may help estimate biochemical remission after surgery for acromegaly. This exploratory perioperative framework may support postoperative risk stratification and patient counseling while complementing standard endocrine follow-up. Further external validation in larger cohorts is warranted.

Indexed as

AcromegalyAdenomaAdultFemaleHumansMachine LearningMaleMiddle AgedPituitary NeoplasmsPredictive Learning ModelsRemission InductionRetrospective StudiesAcromegalyBiochemical remissionMachine learningPituitary adenomaTranssphenoidal surgery

Identifiers

PMID42509491

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.