353 research outputs found
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Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.
We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads
Radiomics in paediatric neuro-oncology : MRI textural features as diagnostic and prognostic biomarkers
Motivation:
Brain and central nervous system tumours form the second most common group of cancers in children in the UK, accounting for 27% of all childhood cancers. Despite current advances in magnetic resonance imaging (MRI), non-invasive characterisation of paediatric brain tumours remains challenging. Radiomics, the high-throughput extraction and analysis of quantitative image features (e.g. texture), offers potential solutions for tumour characterisation and decision support.
Aim and Methods:
In search for diagnostic and prognostic oncological markers, the aim of this thesis was to study the application of MRI texture analysis (TA) for the characterisation of paediatric brain tumours. To this end, single and multi-centre experiments were carried out, within a supervised classification framework, on clinical MR imaging datasets of common brain tumour types.
Results:
TA of conventional MRI was successfully used for diagnostic classification of common paediatric brain tumours. A key contribution of this thesis was to provide evidence that diagnostic classification could be optimised by extending the analysis to include three-dimensional features obtained from multiple MR imaging slices. In addition to this, TA was shown to have a good cross-centre transferability, which is essential for long-term clinical adoption of the technique. Finally, fifteen textural features extracted from T2-weighted MRI were identified to be of significant prognostic value for paediatric medulloblastoma.
Conclusion:
It was shown that MRI TA provides valuable quantifiable information that can supplement qualitative assessments conducted by radiologists, for the characterisation of paediatric brain tumours. TA can potentially have a large clinical impact, since MR imaging is routinely used in the brain cancer clinical work-flow worldwide, providing an opportunity to improve personalised healthcare and decision-support at low cost
Texture analysis of multimodal magnetic resonance images in support of diagnostic classification of childhood brain tumours
Primary brain tumours are recognised as the most common form of solid tumours in children, with pilocytic astrocytoma, medulloblastoma and ependymoma being found most frequently. Despite their high mortality rate, early detection can be facilitated through the use of Magnetic Resonance Imaging (MRI), which is the preferred scanning technique for paediatric patients. MRI offers a variety of imaging sequences through structural and functional imaging, as well as providing complementary tissue information. However visual examination of MR images provides limited ability to characterise distinct histological types of brain tumours. In order to improve diagnostic classification, we explore the use of a computer-aided system based on texture analysis (TA) methods. TA has been applied on conventional MRI but has been less commonly studied on diffusion MRI of brain-related pathology. Furthermore, the combination of textural features derived from both imaging approaches has not yet been widely studied. In this thesis, the aim of the research is to investigate TA based on multi-centre multimodal MRI, in order to provide more comprehensive information and develop an automated processing framework for the classification of childhood brain tumours
Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study
[EN] Objective To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach.
Methods Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one.
Results In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 +/- 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 +/- 0.054) and melanoma BM (eight features, AUC = 0.936 +/- 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 +/- 0.180).
Conclusion Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels.This work has been partially funded by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R. Rafael Ortiz-Ramon was supported by grant ACIF/2015/078 from the Conselleria d'Educacio, Investigacio, Cultura i Esport of the Valencian Community (Spain). Andres Larroza was supported by grant FPU12/01140 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD).Ortiz-Ramón, R.; Larroza-Santacruz, A.; Ruiz-España, S.; Arana Fernandez De Moya, E.; Moratal, D. (2018). Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. 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Clin Radiol 59:1061–1069Kassner A, Thornhill RE (2010) Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol 31:809–816Mahmoud-Ghoneim D, Toussaint G, Constans JM, De Certaines JD (2003) Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987Fetit AE, Novak J, Peet AC, Arvanitis TN (2015) Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumors. NMR Biomed 28:1174–1184Zacharaki EI, Wang S, Chawla S et al (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609–1618Georgiadis P, Cavouras D, Kalatzis I et al (2009) Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods. 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J Magn Reson Imaging 22:674–680Waugh SA, Lerski RA, Bidaut L, Thompson AM (2011) The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. Med Phys 38:5058–5066Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10:266–277Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50:92–98Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496Kuhn M, Johnson K (2013) Data pre-processing. In: Applied predictive modeling, 1st ed. 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Mach Learn 52:199–215Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach. In: 2017 I.E. 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, pp 1213–1216Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Seogwipo, pp 493–496Béresová M, Larroza A, Arana E, et al (2017) 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGMA 1–10Ahmed A, Gibbs P, Pickles M, Turnbull L (2013) Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 38:89–101Chen W, Giger ML, Li H et al (2007) Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58:562–57
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Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.
The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.Funding from project MARESCAN (SAF2011-23870) from Ministerio de Economia y Competitividad in Spain. This work was also partially funded by CIBER-BBN, which is an initiative of the VI National R&D&i Plan 2008-2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. JRG acknowledges support from Cancer Research UK, the University of Cambridge and Hutchison Whampoa Ltd.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/nbm.343
Informatics opportunities and challenges in medical imaging : a journey
The role of the field of informatics in medical imaging is vital; novel or adapted informatics’ core methods can be employed to realise innovative information processing and engineering of medical images. As such, imaging informatics can assist in the interpretation of image-based, clinically recorded evidence. This, in turn, leads to the generation of associated actionable knowledge to achieve precision medicine practice. The discipline of informatics has the power to transform data to useful clinical information patterns of observable evidence and, subsequently to generate actionable knowledge in terms of diagnosis, prognosis, and disease management. This paper presents the author’s personal viewpoint and distinct contributions to innovations in the acquisition and collection of imaging data; storage, retrieval, and management of imaging information objects; quantitative analysis, classification, and dissemination of imaging observable evidence
Proceedings of the International Cancer Imaging Society (ICIS) 16th Annual Teaching Course
Table of contents
O1 Tumour heterogeneity: what does it mean?
Dow-Mu Koh
O2 Skeletal sequelae in adult survivors of childhood cancer
Sue Creviston Kaste
O3 Locoregional effects of breast cancer treatment
Sarah J Vinnicombe
O4 Imaging of cancer therapy-induced CNS toxicity
Giovanni Morana, Andrea Rossi
O5 Screening for lung cancer
Christian J. Herold
O6Risk stratification of lung nodules
Theresa C. McLoud
O7 PET imaging of pulmonary nodules
Kirk A Frey
O8 Transarterial tumour therapy
Bernhard Gebauer
O9 Interventional radiology in paediatric oncology
Derek Roebuck
O10 Image guided prostate interventions
Jurgen J. Fütterer
O11 Imaging cancer predisposition syndromes
Alexander J. Towbin
O12Chest and chest wall masses
Thierry AG Huisman
O13 Abdominal masses: good or bad?
Anne MJB Smets
O14 Hepatobiliary MR contrast: enhanced liver MRI for HCC diagnosis and management
Giovanni Morana
O15 Role of US elastography and multimodality fusion for managing patients with chronic liver disease and HCC
Jeong Min Lee
O16 Opportunities and challenges in imaging metastatic disease
Hersh Chandarana
O17 Diagnosis, treatment monitoring, and follow-up of lymphoma
Marius E. Mayerhoefer, Markus Raderer, Alexander Haug
O18 Managing high-risk and advanced prostate cancer
Matthias Eiber
O19 Immunotherapy: imaging challenges
Bernhard Gebauer
O20 RECIST and RECIST 1.1
Andrea Rockall
O21 Challenges of RECIST in oncology imaging basics for the trainee and novice
Aslam Sohaib
O22 Lymphoma: PET for interim and end of treatment response assessment: a users’ guide to the Deauville Score
Victoria S Warbey
O23 Available resources
Hebert Alberto Vargas
O24 ICIS e-portal and the online learning community
Dow-Mu Koh
O25 Benign lesions that mimic pancreatic cancer
Jay P Heiken
O26 Staging and reporting pancreatic malignancies
Isaac R Francis, Mahmoud, M Al-Hawary, Ravi K Kaza
O27 Intraductal papillary mucinous neoplasm
Giovanni Morana
O28 Cystic pancreatic tumours
Mirko D’Onofrio
O29 Diffusion-weighted imaging of head and neck tumours
Harriet C. Thoeny
O30 Radiation injury in the head and neck
Ann D King
O31 PET/MR of paediatric brain tumours
Giovanni Morana, Arnoldo Piccardo, Maria Luisa Garrè, Andrea Rossi
O32 Structured reporting and beyond
Hebert Alberto Vargas
O33 Massachusetts General Hospital experience with structured reporting
Theresa C. McLoud
O34 The oncologist’s perspective: what the oncologist needs to know
Nick Reed
O35 Towards the cure of all children with cancer: global initiatives in pediatric oncology
Carlos Rodriguez-Galindo
O36 Multiparametric imaging of renal cancers
Hersh Chandarana
O37 Linking imaging features of renal disease and their impact on management strategies
Hebert Alberto Vargas
O38 Adrenals, retroperitoneum and peritoneum
Isaac R Francis, Ashish P Wasnik
O39 Lung and pleura
Stefan Diederich
O40 Advances in MRI
Jurgen J. Fütterer
O41 Advances in molecular imaging
Wim J.G. Oyen
O42 Incorporating advanced imaging, impact on treatment selection and patient outcome
Cheng Lee Chaw, Nicholas van As
S1 Combining ADC-histogram features improves performance of MR diffusion-weighted imaging for Lymph node characterisation in cervical cancer
Igor Vieira, Frederik De Keyzer, Elleke Dresen, Sileny Han, Ignace Vergote, Philippe Moerman, Frederic Amant, Michel Koole, Vincent Vandecaveye
S2 Whole-body diffusion-weighted MRI for surgical planning in patients with colorectal cancer and peritoneal metastases
R Dresen, S De Vuysere, F De Keyzer, E Van Cutsem, A D’Hoore, A Wolthuis, V Vandecaveye
S3 Role of apparent diffusion coefficient (ADC) diffusion-weighted MRI for predicting extra capsular extension of prostate cancer.
P. Pricolo ([email protected]), S. Alessi, P. Summers, E. Tagliabue, G. Petralia
S4 Generating evidence for clinical benefit of PET/CT – are management studies sufficient as surrogate for patient outcome?
C. Pfannenberg, B. Gückel, SC Schüle, AC Müller, S. Kaufmann, N. Schwenzer, M. Reimold,C. la Fougere, K. Nikolaou, P. Martus
S5 Heterogeneity of treatment response in skeletal metastases from breast cancer with 18F-fluoride and 18F-FDG PET
GJ Cook, GK Azad, BP Taylor, M Siddique, J John, J Mansi, M Harries, V Goh
S6 Accuracy of suspicious breast imaging—can we tell the patient?
S Seth, R Burgul, A Seth
S7 Measurement method of tumour volume changes during neoadjuvant chemotherapy affects ability to predict pathological response
S Waugh, N Muhammad Gowdh, C Purdie, A Evans, E Crowe, A Thompson, S Vinnicombe
S8 Diagnostic yield of CT IVU in haematuria screening
F. Arfeen, T. Campion, E. Goldstraw
S9 Percutaneous radiofrequency ablation of unresectable locally advanced pancreatic cancer: preliminary results
D’Onofrio M, Ciaravino V, Crosara S, De Robertis R, Pozzi Mucelli R
S10 Iodine maps from dual energy CT improve detection of metastases in staging examinations of melanoma patients
M. Uhrig, D. Simons, H. Schlemmer
S11Can contrast enhanced CT predict pelvic nodal status in malignant melanoma of the lower limb?
Kate Downey
S12 Current practice in the investigation for suspected Paraneoplastic Neurological Syndromes (PNS) and positive malignancy yield.
S Murdoch, AS Al-adhami, S Viswanathan
P1 Technical success and efficacy of Pulmonary Radiofrequency ablation: an analysis of 207 ablations
S Smith, P Jennings, D Bowers, R Soomal
P2 Lesion control and patient outcome: prospective analysis of radiofrequency abaltion in pulmonary colorectal cancer metastatic disease
S Smith, P Jennings, D Bowers, R Soomal
P3 Hepatocellular carcinoma in a post-TB patient: case of tropical infections and oncologic imaging challenges
TM Mutala, AO Odhiambo, N Harish
P4 Role of apparent diffusion coefficient (ADC) diffusion-weighted MRI for predicting extracapsular extension of prostate cancer
P. Pricolo, S. Alessi, P. Summers, E. Tagliabue, G. Petralia
P5 What a difference a decade makes; comparison of lung biopsies in Glasgow 2005 and 2015
M. Hall, M. Sproule, S. Sheridan
P6 Solid pseudopapillary tumour of pancreas: imaging features of a rare neoplasm
KY Thein, CH Tan, YL Thian, CM Ho
P7 MDCT - pathological correlation in colon adenocarcinoma staging: preliminary experience
S De Luca, C Carrera, V Blanchet, L Alarcón, E Eyheremnedy
P8 Image guided biopsy of thoracic masses and reduction of pneumothorax risk: 25 years experience
B K Choudhury, K Bujarbarua, G Barman
P9 Tumour heterogeneity analysis of 18F-FDG-PET for characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1
GJ Cook, E Lovat, M Siddique, V Goh, R Ferner, VS Warbey
P10 Impact of introduction of vacuum assisted excision (VAE) on screen detected high risk breast lesions
L Potti, B Kaye, A Beattie, K Dutton
P11 Can we reduce prevalent recall rate in breast screening?
AA Seth, F Constantinidis, H Dobson
P12 How to reduce prevalent recall rate? Identifying mammographic lesions with low Positive Predictive Value (PPV)
AA Seth ([email protected]), F Constantinidis, H Dobson
P13 Behaviour of untreated pulmonary thrombus in oncology patients diagnosed with incidental pulmonary embolism on CT
R. Bradley, G. Bozas, G. Avery, A. Stephens, A. Maraveyas
P14 A one-stop lymphoma biopsy service – is it possible?
S Bhuva, CA Johnson, M Subesinghe, N Taylor
P15 Changes in the new TNM classification for lung cancer (8th edition, effective January 2017)
LE Quint, RM Reddy, GP Kalemkerian
P16 Cancer immunotherapy: a review of adequate imaging assessment
G González Zapico, E Gainza Jauregui, R Álvarez Francisco, S Ibáñez Alonso, I Tavera Bahillo, L Múgica Álvarez
P17 Succinate dehydrogenase mutations and their associated tumours
O Francies, R Wheeler, L Childs, A Adams, A Sahdev
P18 Initial experience in the usefulness of dual energy technique in the abdomen
SE De Luca, ME Casalini Vañek, MD Pascuzzi, T Gillanders, PM Ramos, EP Eyheremendy
P19 Recognising the serious complication of Richter’s transformation in CLL patients
C Stove, M Digby
P20 Body diffusion-weighted MRI in oncologic practice: truths, tricks and tips
M. Nazar, M. Wirtz, MD. Pascuzzi, F. Troncoso, F. Saguier, EP. Eyheremendy
P21 Methotrexate-induced leukoencephalopathy in paediatric ALL Patients
D.J. Quint, L. Dang, M. Carlson, S. Leber, F. Silverstein
P22 Pitfalls in oncology CT reporting. A pictorial review
R Rueben, S Viswanathan
P23 Imaging of perineural extension in head and neck tumours
B Nazir, TH Teo, JB Khoo
P24 MRI findings of molecular subtypes of breast cancer: a pictorial primer
K Sharma, N Gupta, B Mathew, T Jeyakumar, K Harkins
P25 When cancer can’t wait! A pictorial review of oncological emergencies
K Sharma, B Mathew, N Gupta, T Jeyakumar, S Joshua
P26 MRI of pancreatic neuroendocrine tumours: an approach to interpretation
D Christodoulou, S Gourtsoyianni, A Jacques, N Griffin, V Goh
P27 Gynaecological cancers in pregnancy: a review of imaging
CA Johnson, J Lee
P28 Suspected paraneoplastic neurological syndromes - review of published recommendations to date, with proposed guideline/flowchart
JA Goodfellow, AS Al-adhami, S Viswanathan
P29 Multi-parametric MRI of the pelvis for suspected local recurrence of prostate cancer after radical prostatectomy
R Bradley
P30 Utilisation of PI-RADS version 2 in multi-parametric MRI of the prostate; 12-months experience
R Bradley
P31 Radiological assessment of the post-chemotherapy liver
A Yong, S Jenkins, G Joseph
P32 Skeletal staging with MRI in breast cancer – what the radiologist needs to know
S Bhuva, K Partington
P33 Perineural spread of lympoma: an educational review of an unusual distribution of disease
CA Johnson, S Bhuva, M Subesinghe, N Taylor
P34 Visually isoattenuating pancreatic adenocarcinoma. Diagnostic imaging tools.
C Carrera, A Zanfardini, S De Luca, L Alarcón, V Blanchet, EP Eyheremendy
P35 Imaging of larynx cancer: when is CT, MRI or FDG PET/CT the best test?
K Cavanagh, E Lauhttp://deepblue.lib.umich.edu/bitstream/2027.42/134651/1/40644_2016_Article_79.pd
Radio-Pathomic Approaches in Pediatric Neurooncology: Opportunities and Challenges
With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models
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