Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI

Koçak, Burak; Durmaz, Emine Şebnem; Kadıoğlu, Pınar; Korkmaz, Özge Polat; Çomunoğlu, Nil; Tanrıöver, Necmettin; Koçer, Naci; Işlak, Civan; Kızılkılıç, Osman

doi:10.1007/s00330-018-5876-2

Cited by 45 publications

(32 citation statements)

References 26 publications

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“…Three studies used the same image database such that only the latest published study was included. At last, this systematic review included 16 studies (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21) (Table 2), with the diagnosis of general pituitary neoplasms, acromegaly, Cushing’s disease, craniopharyngioma and growth hormone deficiency. More than half of the studies were published in the recent 2 years.…”

Section: Resultsmentioning

confidence: 99%

“…Sample size in these studies varied from tens to thousands. The majority of the studies (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20) (76.5%) used the diagnosis of a specific disease as the outcome, only four studies (17, 18, 19, 21) tested on the treatment outcome. In the diagnostic studies, three studies (7, 12, 14) used image features to categorize magnetic resonance images (MRIs), two (6, 13) used face photos to predict acromegaly, two (15, 20) predicted growth home deficiency using serum proteins, two (10, 11) used histological spectrum to predict histology diagnosis, one (9) used serum proteins to predict pituitary adenoma and one (8) predicted surgical phase using videos.…”

Section: Resultsmentioning

confidence: 99%

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A systematic review on machine learning in sellar region diseases: quality and reporting items

Qiao

2019

Endocrine Connections

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Introduction Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies. Methods PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance. Results Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing’s disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results. Conclusions Machine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning in sellar region diseases: quality and reporting items

Qiao

2019

Endocrine Connections

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“…Most of the hitherto performed studies on pre-surgical treatment have been based on unselected patients. The recent development of biomarkers for responsiveness to SRLs (96,97,98,99,100,101,102) in newly diagnosed patients underlines the clinical interest in a proactive approach even before surgery (7). This is also illustrated by the change in strategies over the last decades, as discussed above and where medical treatment has become more prevalent as a first treatment option (2,18,28).…”

Section: Discussion Perspectives and Conclusionmentioning

confidence: 99%

MANAGEMENT OF ENDOCRINE DISEASE: Individualised management of acromegaly

Bollerslev

Heck

Olarescu

2019

European Journal of Endocrinology

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Acromegaly is a rare and challenging disease calling for management in highly specialised multidisciplinary teams (MDTs). Untreated disease has severe morbidity and a clearly increased mortality. Major attainments have been gained over the latest decades, and therefore, the aim of this review is to discuss recent achievements in modern multimodal therapy of acromegaly performed by MDTs, with an emphasis on an individualised, proactive management from the time of diagnosis to long-term outcome. Treatment by surgery is the only potential curative treatment, however, even with modern techniques still with modest cure rates, leaving the patients to often long-term medical treatment. Treatment strategies have changed dramatically in the Western world over recent years, implying a more proactive treatment algorithm often with a shorter or longer pre-surgical treatment period with somatostatin receptor ligands (SRLs). Not all patients will however respond to primary treatment with conventional SRLs and there has recently been a development of potential biomarkers for response that has been implemented in the clinical routine. By today, multimodal treatment can bring every patient in remission, but still almost a third of all patients are undertreated according to large, international registries. On the other hand, it might be a challenge not to over treat thereby bringing the patient into a state of relative or absolute growth hormone deficiency. Clinical series published during the last decade on treatment of patients with acromegaly have indicated a normalisation of mortality, most probably reflecting the proactive and individualised modern treatment. In conclusion, modern, multimodal treatment seems to have normalised mortality, but still the patients suffer from a high multi-organ morbidity and often multipharmacy. Every patient should receive an individualised, proactive treatment in order to improve long-term outcome and to reduce costs for the society.Correspondence should be addressed to J Bollerslev . His special interest is within clinical and translational endocrinology, and in particular, in classical endocrine diseases, such as acromegaly and Cushing's, often studying bone as target tissues for clinical activity. A major topic has been clinical management of acromegaly, and especially searching for biomarkers for responsiveness to medical therapy. Prospectively collected data of newly diagnosed patients with acromegaly has been the background for current studies of individualised, multimodal and transdisciplinary treatment. European Journal of Endocrinology 181:2 R60 Review J Bollerslev and others Managing acromegaly https://eje.bioscientifica.com European Journal of Endocrinology 181:2 R61 Review J Bollerslev and others Managing acromegaly https://eje.bioscientifica.com European Journal of Endocrinology 181:2 R63 Review J Bollerslev and others Managing acromegaly https://eje.bioscientifica.com European Journal of Endocrinology 181:2 R64 Review J Bollerslev and others Managing acromegaly https://eje.bioscie...

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“…Usually, they have higher somatostatin receptor subtype 2 (SST2) protein expression than SGSA with consequent better response to first-generation long-acting somatostatin receptor ligand (SRL) therapy (49,50). Moreover, DGSA usually present hypointensity in T2-weighted MRI, which may be used as a predictor of response to SRL's (51)(52)(53)(54). Comparing to SGSA, patients harboring DGSA have higher age at diagnosis and exhibited longer disease duration before diagnosis, suggesting milder symptomatology (48).…”

Section: Pathologymentioning

confidence: 99%

Acromegaly in the elderly patient

Jallad¹,

Bronstein²

2019

Archives of Endocrinology and Metabolism

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Acromegaly is an insidious disease, usually resulting from growth hormone hypersecretion by a pituitary adenoma. It is most often diagnosed during the 3rd to 4th decade of life. However, recent studies have shown an increase in the incidence and prevalence of acromegaly in the elderly, probably due to increasing life expectancy. As in the younger population with acromegaly, there is a delay in diagnosis, aggravated by the similarities of the aging process with some of the characteristics of the disease. As can be expected elderly patients with acromegaly have a higher prevalence of comorbidities than younger ones. The diagnostic criteria are the same as for younger patients. Surgical treatment of the pituitary adenoma is the primary therapy of choice unless contraindicated. Somatostatin receptor ligands are generally effective as both primary and postoperative treatment. The prognosis correlates inversely with the patient's age, disease duration and last GH level.

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Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI

Cited by 45 publications

References 26 publications

A systematic review on machine learning in sellar region diseases: quality and reporting items

A systematic review on machine learning in sellar region diseases: quality and reporting items

MANAGEMENT OF ENDOCRINE DISEASE: Individualised management of acromegaly

Acromegaly in the elderly patient

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