The PRolaCT studies — a study protocol for a combined randomised clinical trial and observational cohort study design in prolactinoma

Zandbergen, Ingrid M; Najafabadi, Amir H Zamanipoor; Pelsma, Iris C. M.; Marle, M.E. van den Akker-van; Bisschop, Peter H.; Boogaarts, Hieronymus D.; Bon, Arianne C. van; Burhani, B; Cessie, Saskia le; Dekkers, Olaf M.; Drent, Madeleine L.; Feelders, Richard A; Graaf, Johan P. de; Hoogmoed, Jantien; Kapiteijn, Kitty; Klauw, Melanie M. van der; Nieuwlaat, Willy-Anne C M; Pereira, Alberto M.; Stades, Aline M.E.; Ven, Annenienke C van de; Wakelkamp, I. M. M. J.; Furth, Wouter R. van; Biermasz, Nienke R.

doi:10.1186/s13063-021-05604-y

Cited by 9 publications

(15 citation statements)

References 47 publications

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“…Furthermore, pregnancy itself may be associated with subsequent resolution of hyperprolactinaemia (Karunakaran et al 2001), obviating the need for definitive treatment in the form of preconception surgery. The Dutch Prolactinoma Study Group is endeavouring to provide a conclusive answer as to whether upfront surgery is superior to DA treatment in its planned randomised clinical trial entitled 'PRolaCT' (Zandbergen et al 2021). Due to conclude in 2022, this trial involves multiple arms comparing DA treatment against neurosurgical counselling, with surgery in consenting patients, at different time points in patients with a non-invasive prolactinoma.…”

Section: Revival Of Surgery As a First-line Treatmentmentioning

confidence: 99%

Dopamine agonist therapy for prolactinomas: do we need to rethink the place of surgery in prolactinoma management?

Sousa

2022

Endocrine Oncology

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The current treatment paradigm for prolactinomas involves dopamine agonist (DA) therapy as the first-line treatment, with surgical resection reserved for cases where there is DA failure due to resistance or intolerance. This review highlights how DA therapy can be optimised to overcome its increasingly recognised pitfalls, whilst also addressing the potential for expanding the use of surgery in the management of prolactinomas. The first part of the review discusses the limitations of DA therapy, namely: DA resistance; common DA side effects; and the rare but serious DA-induced risks of cardiac valvulopathy, impulse control disorders, psychosis, CSF rhinorrhoea and tumour fibrosis. The second part of the review explores the role of surgery in prolactinoma management with reference to its current second-line position and recent calls for surgery to be considered as an alternative first-line treatment alongside DA therapy. Randomised trials comparing medical versus surgical therapy for prolactinomas are currently underway. Pending these results, a low surgical threshold approach is herein proposed, whereby DA therapy remains the default treatment for prolactinomas unless there are specific triggers to consider surgery, including concern regarding DA side effects or risks in vulnerable patients, persistent and bothersome DA side effects, emergence of any serious risks of DA therapy, expected need for long-term DA therapy, as well as the traditional indications for surgery. This approach should optimise the use of DA therapy for those who will most benefit from it, whilst instituting surgery early in others in order to minimise the cumulative burden of prolonged DA therapy.

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Section: Revival Of Surgery As a First-line Treatmentmentioning

confidence: 99%

Dopamine agonist therapy for prolactinomas: do we need to rethink the place of surgery in prolactinoma management?

Sousa

2022

Endocrine Oncology

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“…In this regard, reporting the number of patients who remain off medication is an important outcome predictor ( 11 , 63 ), as surgery can be an effective alternative treatment option in selected patients ( 11 – 13 , 64 , 65 ). However, whether surgery of prolactinomas dominates DAs as a first-line approach or a second-line treatment is a matter of debate, with the PRolaCT trial hopefully providing insights on this important issue ( 16 ).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, potential long-time effects ( 5 , 6 ), - including personality changes ( 7 – 10 ) - contributed to the increased acceptance of first-line surgery in prolactinomas in recent years ( 11 – 15 ). Although upfront surgery has recently been given a more dominant role in the treatment of prolactinomas ( 16 , 17 ), their indication still remains controversial in selected patients ( 18 , 19 ). Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is crucial to the triage of therapy and interdisciplinary decision-making.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas

et al. 2022

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BackgroundFirst-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making.ObjectiveTo evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia.MethodsBy jointly examining two independent performance metrics – the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) – in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers.ResultsWe demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 – 1.00; MCC: 0.85, 95% CI: 0.60 – 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 – 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 – 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term.ConclusionsThis study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers.

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“…49 [42][43][44][45][46][47][48][49][50][51][52] Role physical 23 [19][20][21][22][23][24][25][26][27][28][29][30] Bodily pain 65 [60][61][62][63][64][65][66][67][68][69][70] General health 28 [22][23][24][25][26][27][28][29][30][31][32][33][34] Social functioning 53 [50][51][52][53][54]…”

Section: Physical Functioningmentioning

confidence: 99%

Patient-reported outcomes in refractory hormone-producing pituitary adenomas: an unmet need

2023

Self Cite

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Purpose To describe quality and outcomes of patient-reported outcome (PRO) measures (PROMs) used in patients with refractory hormone-producing pituitary adenomas, and to provide an overview of PROs in these challenging pituitary adenomas. Methods Three databases were searched for studies reporting on refractory pituitary adenomas. For the purpose of this review, refractory adenomas were defined as tumors resistant to primary therapy. General risk of bias was assessed using a component approach and the quality of PROM reporting was assessed using the International Society for Quality of Life Research (ISOQOL) criteria. Results 20 studies reported on PROMs in refractory pituitary adenomas, using 14 different PROMs, of which 4 were disease specific (median general risk of bias score: 33.5% (range 6–50%) and ISOQOL score: 46% (range 29–62%)). SF-36/RAND-36 and AcroQoL were most frequently used. Health-related quality of life in refractory patients (measured by AcroQoL, SF-36/Rand-36, Tuebingen CD-25, and EQ-5D-5L) varied greatly across studies, and was not always impaired compared to patients in remission. Conclusion There is a scarcity of data on PROs in the subset of pituitary adenomas that is more difficult to treat, e.g., refractory and these patients are difficult to isolate from the total cohort. The patients' perspective on quality of life, therefore, remains largely unknown in refractory patients. Thus, PROs in refractory pituitary adenomas require adequate analysis using properly reported disease specific PROMs in large cohorts to enable appropriate interpretation for use in clinical practice.

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The PRolaCT studies — a study protocol for a combined randomised clinical trial and observational cohort study design in prolactinoma

Cited by 9 publications

References 47 publications

Dopamine agonist therapy for prolactinomas: do we need to rethink the place of surgery in prolactinoma management?

Dopamine agonist therapy for prolactinomas: do we need to rethink the place of surgery in prolactinoma management?

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas

Patient-reported outcomes in refractory hormone-producing pituitary adenomas: an unmet need

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