Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions

Saha, Ashirbani; Tso, Samantha; Rabski, Jessica; Sadeghian, Alireza; Cusimano, Michael D.

doi:10.1007/s11102-019-01026-x

Cited by 28 publications

(18 citation statements)

References 81 publications

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“…The radiomics analysis can be applied to the whole tumor to obtain reproducible, objective, and quantitative data from different imaging sequences, thus providing a more comprehensive method in the approach of various acquired information (13)(14)(15). For application of radiomics in pituitary tumors, Saha et al (41) reported a review article including 16 studies from the past 10 years (2009-2019). Ten of these studies were undertaken from 2018 to 2019, most of which utilized single-centered, retrospective data, semi-automatic pipelines, and binary classifications as in our study.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas

Zhang

Chen

et al. 2020

Front. Oncol.

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ObjectivesA subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs.MethodsOnly patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features.ResultsTwenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001).ConclusionsOur preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.

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Section: Discussionmentioning

confidence: 99%

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas

Zhang

Chen

et al. 2020

Front. Oncol.

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“…CD diagnosis was based on clinical, biochemical, and radiological criteria, according to international guidelines. 23,43,48 Patients who were treatment naïve and those who had already undergone treatment were included. Patients with Nelson's syndrome or silent adrenocorticotropic hormone (ACTH)-secreting adenomas were excluded.…”

Section: Methodsmentioning

confidence: 99%

“…Indeed, after the creation of robust models using training data, ML can enable computers to perform automatic identification of predictive factors and learn complex risk factor interactions for the outcome(s) of interest in new patient cohorts. 12,28,35,[47][48][49] Recent applications of ML to neurosurgery include segmentation of brain tumors and other critical targets (such as basal ganglia for deep brain stimulation), prediction of the location of an epileptic focus based on videoelectroencephalographic monitoring, stratification of clinical outcome after subarachnoid hemorrhage, and prediction of the results of surgery for intraxial brain tumor, discectomy, or laminectomy. 1,3, 13-15, 17, 18, 21, 29-31, 33, 36, 55-60, 63,64 However, difficulties in the interpretation of ML results and the need for specifically trained research support staffs have prevented the wide adoption of ML in neurosurgery.…”

mentioning

confidence: 99%

“…2 Therefore, heterogeneous and sometimes controversial data, coming from biochemical assays, ophthalmological tests, neurological clinical and instrumental evaluations, and MRI and CT scan examinations, performed at diagnosis and follow-ups, have to be integrated and critically analyzed to assess patient outcomes. 25,48,60,61,67 Among the different pituitary afflictions, Cushing disease (CD) is one of the more challenging, with variable rates of remission and high risk of recurrence. 6,27,48,57,66 Therefore, the outcomes of these patients are often the most difficult to predict preoperatively.…”

mentioning

confidence: 99%

“…25,48,60,61,67 Among the different pituitary afflictions, Cushing disease (CD) is one of the more challenging, with variable rates of remission and high risk of recurrence. 6,27,48,57,66 Therefore, the outcomes of these patients are often the most difficult to predict preoperatively. The aim of this study was to evaluate the adoption of ML analysis for the identification of predictive models for the extent of tumor resection and postsurgical and long-term outcomes in a large and consistent cohort of patients with CD treated with an endoscopic endonasal approach (EEA).…”

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confidence: 99%

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Machine learning–based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Zoli

Staartjes

Guaraldi

et al. 2020

Neurosurgical Focus

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OBJECTIVEMachine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD).METHODSAll consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC).RESULTSThe study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81–1.00, accuracy of 81%–100%, and Brier scores of 0.035–0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)–secreting cells were the main predictors for the 3 endpoints of interest.CONCLUSIONSML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.

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Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach

Long,

Ren,

Cheng

et al. 2024

Medical Physics

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BackgroundCushing's Disease (CD) is a rare clinical syndrome characterized by excessive secretion of adrenocorticotrophic hormone, leading to significant functional and structural brain alterations as observed in Magnetic Resonance Imaging (MRI). While traditional statistical analysis has been widely employed to investigate these MRI changes in CD, it has lacked the ability to predict individual‐level outcomes.PurposeTo address this problem, this paper has proposed an interpretable machine learning (ML) framework, including model‐level assessment, feature‐level assessment, and biology‐level assessment to ensure a comprehensive analysis based on structural MRI of CD.MethodsThe ML framework has effectively identified the changes in brain regions in the stage of model‐level assessment, verified the effectiveness of these altered brain regions to predict CD from normal controls in the stage of feature‐level assessment, and carried out a correlation analysis between altered brain regions and clinical symptoms in the stage of biology‐level assessment.ResultsThe experimental results of this study have demonstrated that the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and the opercular portion of the Inferior frontal gyrus of CD showed significant alterations in brain regions. Furthermore, our study has revealed significant correlations between clinical symptoms and the frontotemporal lobes, insulin, and olfactory cortex, which also have been confirmed by previous studies.ConclusionsThe ML framework proposed in this study exhibits exceptional potential in uncovering the intricate pathophysiological mechanisms underlying CD, with potential applicability in diagnosing other diseases.

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Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions

Cited by 28 publications

References 81 publications

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas

Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas

Machine learning–based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach

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