Pitfalls in early biochemical evaluation after transsphenoidal surgery in patients with acromegaly

Nishioka, Hiroshi; Fukuhara, Noriaki; Yamaguchi-Okada, Mitsuo; Takeshita, Akira; Takeuchi, Yasuhiro; Yamada, Shozo

doi:10.1507/endocrj.ej17-0261

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…The underlying mechanism of delayed remission in acromegaly after TSS remains unclear. One possible hypothesis for delayed remission is that it takes longer than expected for IGF1 levels to return to normal (7). Another hypothesis is that there are still some residual GH-secreting tumor cells after pituitary adenoma resection.…”

Section: Discussionmentioning

confidence: 99%

“…According to the literature and our clinical experience, some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remission (5,6). Changes in GH and IGF1 levels may be inconsistent following surgery, and the reason for delayed remission may be a longer-than-expected period required for IGF1 levels to return to normal (7). The reason may also be that the residual tumor cells gradually necrotize with ischemia after surgery.…”

Section: Introductionmentioning

confidence: 99%

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Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Dai

Fan

et al. 2020

Front. Endocrinol.

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Background: Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remission (DR). DR may complicate the interpretation of surgical outcomes in patients with acromegaly and interfere with decision-making regarding postoperative adjuvant therapy. Objective: We aimed to develop and validate machine learning (ML) models for predicting DR in acromegaly patients who have not achieved remission within 6 months of surgery. Methods: We enrolled 306 acromegaly patients and randomly divided them into training and test datasets. We used the recursive feature elimination (RFE) algorithm to select features and applied six ML algorithms to construct DR prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. We used permutation importance, SHapley Additive exPlanations (SHAP), and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. Results: Fifty-five (17.97%) acromegaly patients met the criteria for DR, and five features (post-1w rGH, post-1w nGH, post-6m rGH, post-6m IGF-1, and post-6m nGH) were significantly associated with DR in both the training and the test datasets. After the RFE feature selection, the XGboost model, which comprised the 15 important features, had the greatest discriminatory ability (area under the curve = 0.8349, sensitivity = 0.8889, Youden's index = 0.6842). The XGboost model showed good discrimination ability and provided significantly better estimates of DR of patients with acromegaly compared with using only the Knosp grade. The results obtained from permutation importance, SHAP, and LIME algorithms showed that post-6m IGF-1 is the most important feature in XGboost algorithm prediction and showed the reliability and the clinical practicability of the XGboost model in DR prediction. Dai et al. ML Models Predict Acromegaly DR Conclusions: ML-based models can serve as an effective non-invasive approach to predicting DR and could aid in determining individual treatment and follow-up strategies for acromegaly patients who have not achieved remission within 6 months of surgery.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Dai

Fan

et al. 2020

Front. Endocrinol.

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“…Finally, according to our definition of biochemical remission after surgery, we included several patients with normal GH and elevated IGF-1 levels in the remission group, which may have interfered with the results. Although IGF-1 may revert to the normal range after several months and show a delayed remission change [16,17,47], the PASQ scores in patients with discordant GH and IGF-1 levels in the short term after surgery are still unknown.…”

Section: Endocrine Journal Advance Publicationmentioning

confidence: 99%

Self-reported symptoms in patients with acromegaly: a 6-month follow-up in a single neurosurgical center

Lin

Chen

et al. 2023

Endocr J

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“…In fact, in comparison with GH, the decline in IGF1 is delayed due to the longer half-life of IGF-binding proteins. Nadir GH after an OGTT may slightly change over time, from immediately postoperatively to 3 months after NS [8]. A prospective study evaluated the predictive role of random GH and IGF1 on a postoperative day one and nadir GH following an OGTT 2 − 4 days after NS [9].…”

Section: Introductionmentioning

confidence: 99%

First but not second postoperative day growth hormone assessments as early predictive tests for long-term acromegaly persistence

Cambria

Beccuti

Prencipe

et al. 2021

J Endocrinol Invest

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Purpose Postoperative assessment of acromegaly activity is typically performed at least 3 months after neurosurgery (NS). Few studies have evaluated the use of early postoperative growth hormone (GH) levels as a test to predict short- and long-term remission of acromegaly. Our objective was to evaluate the diagnostic performance of serum random GH on a postoperative day one (D1-rGH) and two (D2-rGH), particularly in predicting long-term disease persistence. Materials and methods Forty-one subjects with acromegaly who were undergoing NS were enrolled (mean age ± SD 47.4 ± 13.1 years at diagnosis; women 54%; macroadenomas 71%). The final assessment of disease activity was performed one year after NS. ROC curves were used to evaluate the diagnostic performance of D1-rGH and D2-rGH. Results After a 1-year follow-up, the overall remission rate was 55%. ROC analysis identified an optimal D1-rGH cut-off value of 2.1 ng/mL for diagnosing long-term disease persistence (55.6% SE; 90.9% SP). The cut-off point became 2.5 ng/mL after maximizing specificity for disease persistence (yielding a 100% positive predictive value) and 0.3 ng/mL after maximizing sensitivity for disease remission. The optimal D2-rGH cut-off value was 0.6 ng/mL (81.8% SE; 50% SP); the cut-off point became 2.9 ng/mL after maximizing specificity and 0.1 ng/mL after maximizing sensitivity, with no clinical utility. Conclusions D1-rGH could be a highly specific test for the early diagnosis of long-term acromegaly persistence, which is predicted by a value > 2.5 ng/mL with a great degree of certainty. The diagnostic performance of D2-rGH was insufficient. Further research is required to validate these preliminary results prior to modifying the postoperative management of acromegaly.

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Pitfalls in early biochemical evaluation after transsphenoidal surgery in patients with acromegaly

Cited by 10 publications

References 27 publications

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Self-reported symptoms in patients with acromegaly: a 6-month follow-up in a single neurosurgical center

First but not second postoperative day growth hormone assessments as early predictive tests for long-term acromegaly persistence

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