Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands

2021 ◽  
Author(s):  
Luiz Eduardo Wildemberg ◽  
Aline Helen da Silva Camacho ◽  
Renan Lyra Miranda ◽  
Paula C L Elias ◽  
Nina R de Castro Musolino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Predictive Value ◽  
First Generation ◽  
Somatostatin Receptor ◽  
Support Vector ◽  
Receptor Ligands ◽  
Somatostatin Receptor Ligands ◽  
Igf I

Abstract Context Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly. Objective To develop a prediction model of therapeutic response of acromegaly to fg-SRL. Methods Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented growth hormone (GH) <1.0 ng/mL and normal age-adjusted insulin-like growth factor (IGF)-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest, and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH, and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP). Results A total of 153 patients were analyzed. Controlled patients were older (P = .002), had lower GH at diagnosis (P = .01), had lower pretreatment GH and IGF-I (P < .001), and more frequently harbored tumors that were densely granulated (P = .014) or highly expressed SST2 (P < .001). The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%. Conclusion We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality, and reduce health services costs.

scholarly journals MON-311 Glucose Metabolism in Acromegaly Patients Resistant to First Generation Somatostatin Receptor Ligands Treated with Pegvisomant And/Or Pasireotide Lar

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Sabrina Chiloiro ◽  
Antonella Giampietro ◽  
Antonio Bianchi ◽  
Felicia Visconti ◽  
Anna Maria Formenti ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
First Generation ◽  
Systemic Disease ◽  
Somatostatin Receptor ◽  
High Growth ◽  
Study Entry ◽  
Close Monitoring ◽  
Receptor Ligands ◽  
Somatostatin Receptor Ligands ◽  
Igf I

Abstract Introduction: Acromegaly (Acro) is a systemic disease characterized by high growth hormone (GH) and insulin like growth factor-I (IGF-I), insulin resistance, glucose intolerance (IGT) and higher diabetes mellitus (DM) risk in 15% - 38% of patients (pts). Moreover, different medical therapies of Acro are reported to have variable effects on glucose metabolism. An association between blood glucose (BG) and serum IGF-I levels in patients with DM and Acro has been suggested, while IGF-I levels and hemoglobin A1c (HbA1c) correlation is still controversial because of the multifactorial influence.Study aim: to investigate glucose metabolism in pts with Acro resistant to 1st gen somatostatin receptor ligands (SRLs) treated with Pegvisomant (Peg) or Pasireotide LAR (Pasi). Patients and Methods: Retrospective, international, multicenter study; consecutive pts enrolled according to following inclusion criteria for at least 6 consecutive months: (1) resistant to 1st gen SRLs, (2) treated with Pasi or Peg for active Acro. Patients with concomitant treatments with known action on glucose metabolism were excluded, with the exception of glucocorticoid replacement for central hypoadrenalism. Results: 72 pts with active Acro, mean age at study entry 37 ±15 yrs, 47 females (65.3%). 28 (38.9%) pts were treated with Pasi and 44 pts with Peg (61.1%). Peg was monotherapy in 18 pts (40.9%) and in combo with first generation SRLs for 26 pts (59.1%). The number of pts with IGT and DM2 was superimposable between the 2 groups (Pasi and Peg). In Pasi group, 19 pts had Acro control (67.9%); glucose metabolism worsened in 16 pts (57.1%). Worsening of glucose metabolism occurred most frequently in pts with persistently active Acro (62.5%) and in pts with higher BG and HbA1c values at study start. Similarly, HbA1c was higher in pts with active Acro, although HbA1c worsened during Pasi treatment both in euglycemic and IGT at study entry, regardless of Acro control. In Peg group, 31 pts reached Acro control (73%); glucose metabolism worsened in 12 (27.3%) but improved in 5 pts (11.4%). All pts who experienced glucose metabolism improvement had controlled Acro, regardless of the use of a combo with first generation SRL. Among the 13 pts with active Acro after Peg, BG worsened in 5 cases (38.4%). Moreover, we found that pts with worsening BG control had higher HbA1c (p=0.03) and required higher Peg doses (mean ±SD 25 ±10 mg/day; p=0.04). Patients with higher HbA1c had higher IGF-I, both at study entry and at study end and were treated with higher Peg dose (mean 25 mg/day). Conclusion: Impaired glucose metabolism was more frequent after Pasireotide treatment and in patients of both Pasireotide and Pegvisomant groups with altered pre-treatment glucose and persistently active disease. Therefore, in such acromegaly patients close monitoring of glucose status is recommended during treatment.

MRI follow‐up of patients with acromegaly treated after surgery with first‐generation somatostatin receptor ligands

2021 ◽  
Author(s):  
Naia Grandgeorge ◽  
Giovanni Barchetti ◽  
Solange Grunenwald ◽  
Fabrice Bonneville ◽  
Philippe Caron
Keyword(s):  
First Generation ◽  
Somatostatin Receptor ◽  
Receptor Ligands ◽  
Somatostatin Receptor Ligands

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals The future of somatostatin receptor ligands in acromegaly

2021 ◽  
Author(s):  
Monica R Gadelha ◽  
Luiz Eduardo Wildemberg ◽  
Leandro Kasuki
Keyword(s):  
Precision Medicine ◽  
First Generation ◽  
Somatostatin Receptor ◽  
New Drugs ◽  
Response To Treatment ◽  
Tumor Shrinkage ◽  
Receptor Ligands ◽  
Biochemical Control ◽  
Somatostatin Receptor Ligands ◽  
The Future

Abstract Currently, first-generation somatostatin receptor ligands (fg-SRLs), octreotide LAR and lanreotide autogel, are the mainstays of acromegaly treatment and achieve biochemical control in approximately 40% of patients and tumor shrinkage in over 60% of patients. Pasireotide, a second-generation SRL, shows higher efficacy with respect to both biochemical control and tumor shrinkage but has a worse safety profile. In this review, we discuss the future perspectives of currently available SRLs, focusing on the use of biomarkers of response and precision medicine, new formulations of these SRLs and new drugs, which are under development. Precision medicine, which is based on biomarkers of response to treatment, will help guide the decision-making process by allowing physicians to choose the appropriate drug for each patient and improving response rates. New formulations of available SRLs, such as oral, subcutaneous depot and nasal octreotide, may improve patients’ adherence to treatment and quality of life since there will be more options available that better suit each patient. Finally, new drugs, such as paltusotine, somatropin, ONO-5788 and ONO-ST-468, may improve treatment adherence and present higher efficacy than currently available drugs.

scholarly journals Building a Cardiovascular Disease Prediction Model for Smartwatch Users Using Machine Learning: Based on the Korea National Health and Nutrition Examination Survey

Biosensors ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 228
Author(s):  
Min-Jeong Kim
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Support Vector Machine ◽  
Prediction Model ◽  
National Health ◽  
Support Vector ◽  
Nutrition Examination Survey ◽  
Related Data ◽  
Health And Nutrition ◽  
Health Related

Smartwatches have the potential to support health care in everyday life by supporting self-monitoring of health conditions and personal activities. This paper aims to develop a model that predicts the prevalence of cardiovascular disease using health-related data that can be easily measured by smartwatch users. To this end, the data corresponding to the health-related data variables provided by the smartwatch are selected from the Korea National Health and Nutrition Examination Survey. To classify the prevalence of cardiovascular disease with these selected variables, we apply logistic regression, artificial neural network, and support vector machine among machine learning classification techniques, and compare the appropriateness of the algorithm through classification performance indicators. The prediction model using support vector machine showed the highest accuracy. Next, we analyze which structures or parameters of the support vector machine contribute to increasing accuracy and derive the importance of input variables. Since it is very important to diagnose cardiovascular disease early correctly, we expect that this model will be very useful if there is a tool to predict whether cardiovascular disease develops or not.

scholarly journals How to Guarantee Food Safety via Grain Storage? An Approach to Improve Management Effectiveness by Machine Learning Algorithms

2021 ◽  
Vol 2 (8) ◽  
pp. 675-684
Author(s):  
Jin Wang ◽  
Youjun Jiang ◽  
Li Li ◽  
Chao Yang ◽  
Ke Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Grain Storage ◽  
Management Effectiveness

The purpose of grain storage management is to dynamically analyze the quality change of the reserved grains, adopt scientific and effective management methods to delay the speed of the quality deterioration, and reduce the loss rate during storage. At present, the supervision of the grain quality in the reserve mainly depends on the periodic measurements of the quality of the grains and the milled products. The data obtained by the above approach is accurate and reliable, but the workload is too large while the frequency is high. The obtained conclusions are also limited to the studied area and not applicable to be extended into other scenarios. Therefore, there is an urgent need of a general method that can quickly predict the quality of grains given different species, regions and storage periods based on historical data. In this study, we introduced Back-Propagation (BP) neural network algorithm and support vector machine algorithm into the quality prediction of the reserved grains. We used quality index, temperature and humidity data to build both an intertemporal prediction model and a synchronous prediction model. The results show that the BP neural network based on the storage characters from the first three periods can accurately predict the key storage characters intertemporally. The support vector machine can provide precise predictions of the key storage characters synchronously. The average predictive error for each of wheat, rice and corn is less than 15%, while the one for soybean is about 20%, all of which can meet the practical demands. In conclusion, the machine learning algorithms are helpful to improve the management effectiveness of grain storage.

scholarly journals A Phase 3 Large International Noninferiority Trial (MPOWERED): Assessing Maintenance of Response to Oral Octreotide Capsules in Comparison to Injectable Somatostatin Receptor Ligands

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A517-A517
Author(s):  
Maria Fleseriu ◽  
Alexander V Dreval ◽  
Yulia Pokramovich ◽  
Irina Bondar ◽  
Elena Isaeva ◽  
...  
Keyword(s):  
Somatostatin Receptor ◽  
Safety Data ◽  
Biochemical Response ◽  
Receptor Ligands ◽  
Phase 3 ◽  
Landmark Analysis ◽  
End Point ◽  
Noninferiority Trial ◽  
Somatostatin Receptor Ligands ◽  
Igf I

Abstract Background: MPOWERED, a large phase 3 trial, assessed maintenance of response to oral octreotide capsules (OOC; MYCAPSSA®) compared to injectable somatostatin receptor ligands (iSRLs) in patients with acromegaly who responded to OOC and iSRLs (octreotide or lanreotide). OOC were recently approved in the US for patients with acromegaly who responded to and tolerated iSRLs. Methods: Eligibility criteria included age 18-75 years at screening, acromegaly diagnosis, disease evidence, biochemical control (insulin-like growth factor I [IGF-I] <1.3 × upper limit of normal [ULN] and mean integrated growth hormone [GH] <2.5 ng/mL) at screening, and ≥6 months’ iSRL treatment. Effective OOC dose was determined in a 26-week Run-in phase. Eligible patients (IGF-I <1.3 × ULN and mean integrated GH <2.5 ng/mL, week 24) were randomized to a 36-week controlled treatment phase (RCT), receiving OOC or iSRLs starting at week 26. The primary end point was a noninferiority assessment of proportion of patients biochemically controlled in the RCT (IGF-I <1.3 × ULN using time-weighted average). Other end points included nonresponse imputation of the primary end point, landmark analysis using proportion of responders based on average of last 2 IGF-I values at end of RCT, and change from baseline RCT (week 26) IGF-I and GH levels. Results: Of 146 enrolled patients, 92 entered the RCT (OOC, n=55; iSRLs, n=37). Both arms were well balanced for age, sex, and acromegaly duration. OOC demonstrated noninferiority to iSRLs in maintaining biochemical response, with 91% (CI, 80%-97%) of OOC and 100% (CI, 91%-100%) of iSRL groups maintaining control during the RCT. Of those responding at end of Run-in, 96% of patients on OOC maintained response during RCT. Using nonresponse imputation, 89% of OOC and 95% of iSRL groups were biochemically controlled in RCT. Landmark analysis of those respnding at end of Run-in showed that 94% of patients in each group maintained response at RCT end. In both groups, IGF-I levels were stable in the RCT, average IGF-I at baseline and RCT end being 0.9 × ULN (OOC) and 0.8 × ULN (iSRL). Mean change in GH from RCT start to RCT end was -0.03 ng/mL (OOC) and +0.29 ng/mL (iSRL). Safety data were mostly similar between groups; the OOC group did not experience injection site reactions. Conclusion: In this noninferiority trial in patients with acromegaly, OOC demonstrated maintenance of biochemical response compared to iSRLs. Results support the efficacy of OOC as a possible iSRL alternative.

scholarly journals gsp Mutation Is Not a Molecular Biomarker of Long-Term Response to First-Generation Somatostatin Receptor Ligands in Acromegaly

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4857
Author(s):  
Luiz Eduardo Wildemberg ◽  
Daniel Henriques ◽  
Paula C. L. Elias ◽  
Carlos Henrique de A. Lima ◽  
Nina R. de Castro Musolino ◽  
...  
Keyword(s):  
First Generation ◽  
Somatostatin Receptor ◽  
Receptor Ligands ◽  
Biochemical Control ◽  
Molecular Biomarker ◽  
Α Subunit ◽  
Cavernous Sinus Invasion ◽  
Somatostatin Receptor Ligands ◽  
Term Response

Background: It is still controversial if activating mutations in the stimulatory G-protein α subunit (gsp mutation) are a biomarker of response to first generation somatostatin receptor ligands (fg-SRL) treatment in acromegaly. Thus, we aimed to evaluate whether gsp mutation predicts long-term response to fg-SRL treatment and to characterize the phenotype of patients harboring gsp mutations. Methods: GNAS1 sequencing was performed by Sanger. SST2 and SST5 were analyzed by immunohistochemistry (IHC) and real-time RT-PCR. The cytokeratin granulation pattern was evaluated by IHC. Biochemical control was defined as GH < 1.0 ng/mL and normal age-adjusted IGF-I levels. Results: gsp mutation was found in 54 out of 136 patients evaluated. Biochemical control with fg-SRL treatment was similar in gsp+ and gsp- patients (37% vs. 25%, p = 0.219). Tumors harboring gsp mutation were smaller (p = 0.035) and had a lower chance of invading cavernous sinuses (p = 0.001). SST5 protein (p = 0.047) and mRNA (p = 0.013) expression levels were higher in wild-type tumors. Conclusions: In this largest series available in the literature, we concluded that gsp is not a molecular biomarker of response to fg-SRL treatment in acromegaly. However, the importance of its negative association with cavernous sinus invasion and SST5 expression needs to be further investigated.

scholarly journals Pasireotide in the Personalized Treatment of Acromegaly

2021 ◽  
Vol 12 ◽  
Author(s):  
Manel Puig-Domingo ◽  
Ignacio Bernabéu ◽  
Antonio Picó ◽  
Betina Biagetti ◽  
Joan Gil ◽  
...  
Keyword(s):  
First Generation ◽  
Somatostatin Receptor ◽  
Receptor Ligands ◽  
First Line ◽  
First Line Therapy ◽  
Somatostatin Receptor Ligands ◽  
Therapeutic Algorithm ◽  
Surgical Failure ◽  
Positive Effect ◽  
T2 Mri

The delay in controlling the disease in patients who do not respond to first-line treatment with first generation somatostatin receptor ligands (first-generation SRLs) can be quantified in years, as every modification in the medical therapy requires some months to be fully evaluated. Considering this, acromegaly treatment should benefit from personalized medicine therapeutic approach by using biomarkers identifying drug response. Pasireotide has been positioned mostly as a compound to be used in first-generation SRLs resistant patients and after surgical failure, but sufficient data are now available to indicate it is a first line therapy for patients with certain characteristics. Pasireotide has been proved to be useful in patients in which hyperintensity T2 MRI signal is shown and in those depicting low SST2 and high expression of SST5, low or mutated AIP condition and sparsely granulated immunohistochemical pattern. This combination of clinical and pathological characteristics is unique for certain patients and seems to cluster in the same cases, strongly suggesting an etiopathogenic link. Thus, in this paper we propose to include this clinico-pathologic phenotype in the therapeutic algorithm, which would allow us to use as first line medical treatment those compounds with the highest potential for achieving the fastest control of GH hypersecretion as well as a positive effect upon tumor shrinkage, therefore accelerating the implementation of precision medicine for acromegaly. Moreover, we suggest the development, validation and clinical use of a pasireotide acute test, able to identify patients responsive to pasireotide LAR as the acute octreotide test is able to do for SRLs.

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