Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging

Giardina, Gabriel; Micko, Alexander; Bovenkamp, Daniela; Krause, Arno; Placzek, Fabian; Papp, László; Krajnc, Denis; Spielvogel, Clemens P.; Winklehner, Michael; Höftberger, Romana; Vila, Greisa; Andreana, Marco; Leitgeb, Rainer A.; Drexler, Wolfgang; Wolfsberger, Stefan; Unterhuber, Angelika

doi:10.3390/cancers13133234

Cited by 12 publications

(6 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the radiomic model based on depth characteristics of WSIs in predicting glioma grading (LGG and high-grade glioma) ( 147 ), liver cancer subtypes ( 148 ), and molecular subtypes of bladder cancer ( 90 ) has reached the level that can be assessed by pathologists. Multimodality spectral imaging (optical coherence tomography, malignant pleural mesothelioma, and LSRM) for morphology-molecular metabolism analytics has distinguished the pituitary from tumor and classified pituitary adenoma subtypes ( 125 ).…”

Section: Discussionmentioning

confidence: 99%

“…Alvarez-Jimenez et al (124) predicted patients with adenocarcinoma and squamous cell carcinoma by WSI and CTbased traits and found essential correlation attributes across scales between pathological sections and CT images to significantly improve the identification of NSCLC subtypes. Giardina et al (125) assessed the value of profiles of optical coherence tomography, multiphoton microscopy, and line scan Raman microspectroscopy to analyze and identify morphomolecular metabolic. The accuracy of the suggested model was as high as 88% and 99% for the binary task of identifying gland and adenomas and the multivariate task of distinguishing pituitary adenoma subtypes, respectively.…”

Section: Multimodality Analysismentioning

confidence: 99%

“…Giardina et al. ( 125 ) assessed the value of profiles of optical coherence tomography, multiphoton microscopy, and line scan Raman microspectroscopy to analyze and identify morpho-molecular metabolic. The accuracy of the suggested model was as high as 88% and 99% for the binary task of identifying gland and adenomas and the multivariate task of distinguishing pituitary adenoma subtypes, respectively.…”

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Multimodality Analysismentioning

confidence: 99%

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…However, the presence of Crooke cells in corticotroph adenomas could limit the differentiation by OCT of pituitary adenoma and pituitary gland in these tumors. Therefore, CNN training and additional information about metabolic or molecular information could help to identify tumor/gland in this kind of tumor subtypes ( 40 , 47 ).…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Pituitary Adenoma Biopsies by Ultrahigh Resolution Optical Coherence Tomography Using Neuronal Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

ObjectiveDespite advancements of intraoperative visualization, the difficulty to visually distinguish adenoma from adjacent pituitary gland due to textural similarities may lead to incomplete adenoma resection or impairment of pituitary function. The aim of this study was to investigate optical coherence tomography (OCT) imaging in combination with a convolutional neural network (CNN) for objectively identify pituitary adenoma tissue in an ex vivo setting.MethodsA prospective study was conducted to train and test a CNN algorithm to identify pituitary adenoma tissue in OCT images of adenoma and adjacent pituitary gland samples. From each sample, 500 slices of adjacent cross-sectional OCT images were used for CNN classification.ResultsOCT data acquisition was feasible in 19/20 (95%) patients. The 16.000 OCT slices of 16/19 of cases were employed for creating a trained CNN algorithm (70% for training, 15% for validating the classifier). Thereafter, the classifier was tested on the paired samples of three patients (3.000 slices). The CNN correctly predicted adenoma in the 3 adenoma samples (98%, 100% and 84% respectively), and correctly predicted gland and transition zone in the 3 samples from the adjacent pituitary gland.ConclusionTrained convolutional neural network computing has the potential for fast and objective identification of pituitary adenoma tissue in OCT images with high sensitivity ex vivo. However, further investigation with larger number of samples is required.

show abstract

“…[15][16][17][18] A transcriptomic and a radiomic study were excluded. 19,20 A study performed on animal cell cultures was removed. 21 A study that investigated PAs without specifying which of those had GH hypersecretion was excluded.…”

Section: Literature Searchmentioning

confidence: 99%

Metabolomics in acromegaly: a systematic review

Pînzariu

Georgescu

2023

Journal of Investigative Medicine

View full text Add to dashboard Cite

The therapeutic response heterogeneity in acromegaly persists, despite the medical–surgical advances of recent years. Thus, personalized medicine implementation, which focuses on each patient, is justified. Metabolomics would decipher the molecular mechanisms underlying the therapeutic response heterogeneity. Identification of altered metabolic pathways would open new horizons in the therapeutic management of acromegaly. This research aimed to evaluate the metabolomic profile in acromegaly and metabolomics’ contributions to understanding disease pathogenesis. A systematic review was carried out by querying four electronic databases and evaluating patients with acromegaly through metabolomic techniques. In all, 21 studies containing 362 patients were eligible. Choline, the ubiquitous metabolite identified in growth hormone (GH)-secreting pituitary adenomas (Pas) by in vivo magnetic resonance spectroscopy (MRS), negatively correlated with somatostatin receptors type 2 expression and positively correlated with magnetic resonance imaging T2 signal and Ki-67 index. Moreover, elevated choline and choline/creatine ratio differentiated between sparsely and densely granulated GH-secreting PAs. MRS detected low hepatic lipid content in active acromegaly, which increased after disease control. The panel of metabolites of acromegaly deciphered by mass spectrometry (MS)-based techniques mainly included amino acids (especially branched-chain amino acids and taurine), glyceric acid, and lipids. The most altered pathways in acromegaly were the metabolism of glucose (particularly the downregulation of the pentose phosphate pathway), linoleic acid, sphingolipids, glycerophospholipids, arginine/proline, and taurine/hypotaurine. Matrix-assisted laser desorption/ionization coupled with MS imaging confirmed the functional nature of GH-secreting PAs and accurately discriminated PAs from healthy pituitary tissue.

show abstract

Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging

Cited by 12 publications

References 62 publications

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Diagnosis of Pituitary Adenoma Biopsies by Ultrahigh Resolution Optical Coherence Tomography Using Neuronal Networks

Metabolomics in acromegaly: a systematic review

Contact Info

Product

Resources

About