LUNG CANCER

LUNG CANCER

Lung cancer is usually diagnosed in advanced stages with majority of patients already presenting metastatic disease. Only 20 % of NSCLC patients have early stage disease at the time of diagnosis, thus being potentially resectable. The current gold standard is lobectomy with hilar and mediastinal lymph-node sampling or dissection. Curative stereotactic body radiotherapy (SABR) should be offered to patients with stage I NSCLC who have clinical comorbidities or are at very high surgery-related risk, and those who refuse to undergo surgical procedure. Post-operative platinum-based chemotherapy is recommended for all patients with stage II and III surgically resected disease. For patients with locally advanced stage NSCLS, there are different recommended options, including

  • Surgery followed by adjuvant chemotherapy
  • Neoadjuvant chemotherapy followed by surgery
  • Neoadjuvant chemo-radiation followed by surgery

Eligibility for pre-operative or post-operative platinum-doublets with or without radiotherapy should be evaluated in the context of an experienced multidisciplinary team.

The diagnostic evaluation initially focuses on careful physical examination and patient’s history, to identify new symptoms or a significant change in the common respiratory symptoms. In clinical trials, all patients with suspected lung cancer will undergo a non-invasive chest imaging, including X-rays, CT-scan, and if needed positron emission tomography (PET) with fluorodeoxyglucose (FDG).

Conventional contrast-enhanced chest CT-scan is considered the best exam to detect lung cancer, as it provides detailed information on anatomic location, margins, invasion of surrounding structures or chest wall, and mediastinal lymph nodes involvement.

Use of imaging presents a great opportunity for selecting the right patients and enriching the trial.

IAG’ team has extensive experience in using

  • CT
  • PET
  • MRI

in early and late phase studies.

CT scans are normally performed at the screening and follow-up visits using standardized imaging equipment. At IAG, we design and implement protocols for thoracic CT images, assess the need for breath hold and use of intravenous contrast. We support image reconstruction, quality control and reading.

To optimise nodule detection, normally all baseline CTs are read by experienced thoracic radiologists; sometimes located in the local trial centres or at a central site. All discrepancies and adjudication is done by IAG’s imaging specialists.  It is common to ask the readers to identify and record all lung nodules greater than a certain size and diameter. We deploy volumetric measures whenever possible. Our measurements include the volume and maximum intensity projections (MIPs) to aid the detection.

MRI exams are done in addition to CT  when we are trying to assess the drug efficacy in a specific way, such as 1) resolution of the tumour invasion into the chest wall and the mediastinal structures (pancoast tumour); 2) impact on the solid and vascular hilar masses; 3) impact on the diaphragmatic abnormalities or when following-up mediastinal lymphoma.

MR exams are susceptible to motion artefacts such as breathing and require specific software to process. Recently, new applications, such as whole-body MR (WBMR) imaging are being deployed to assess metastatic disease. Diffusion weighted imaging (DWI) is used to assess changes in the tumour cellularity and the integrity of the cellular membrane. The DWI sequence is made susceptible to the differences in water mobility. The motion of water molecules is more restricted in tissues with a high cellular density associated with numerous intact cell membranes (e.g. tumour tissue). This technique can be applied for tumour detection and tumour characterisation and for the monitoring of response to treatment.

PET/CT is a combined imaging technique: CT giving anatomical information and PET giving metabolic information to detect lesions initially not seen on CT and to assess more precise localisation of lesions, delineate them from their surrounding structures and provide more accurate characterisation of a lesion as benign or malignant.

Reach out to our expert team, as you are designing and planning your trial.

About IAG, Image Analysis Group

IAG, Image Analysis Group is a strategic partner to bio-pharmaceutical companies developing new treatments to improve patients’ lives. Our dynamic Strategy, Trial Solutions and Bio-Partnering divisions work closely to meet critical needs of biotechnology companies: funding, clinical development, and monetization of their assets. We fuse decades of therapeutic insights, risk-sharing business model and agile culture to accelerate novel drug development. IAG broadly leverages its core imaging expertise, proprietary technology platform DYNAMIKA and capabilities to support an objective early go no/ go decision and drive excellence for tomorrow’s innovative therapeutic agents with speed.

Contact our expert team: imaging.experts @ ia-grp.com

 

 

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Experience: Scoring Systems
  • RECIST1.1
  • iRECIST
Experience: Imaging
  • CT
  • PET/CT
  • MRI
  • DWI
  • WBMRI
  • PET
Publications

Since 2007, over 2000 articles were published to cover scientific discoveries, technology break-throughs and special cases. We list here some critically important papers and abstracts.

Testimonials

Combining our technologies and business advisory services with promising life science companies has yielded spectacular results over the past five years. As a trusted partner to many biotech and pharma companies, IAG’s team is proud to share your words and quotes.

Decision Making in Surveillance of High-Grade Gliomas Using Perfusion MRI as Adjunct to Conventional MRI and Artificial Intelligence.

Copyright © 2019 by American Society of Clinical Oncology
Journal of Clinical Oncology. 2019 May;37(15)_suppl doi: 10.1200/JCO.2019.37.15_suppl.2054

Abstract

BACKGROUND:
Surveillance of High-Grade Gliomas (HGGs) remains a major challenge in clinical neurooncology. Histopathological validation is not an option during the course of disease and imaging surveillance suffers from ambiguous features of both disease progression and treatment related changes. This study aimed to differentiate between Pseudoprogression (PsP) and Progressive Disease (PD) using an artificial intelligence (support vector machine – SVM) classification algorithm.
METHODS:
Two groups of patients with histologically proven HGGs were analysed, a group with a single time point DSC perfusion MRI (45 patients) and a group with multiple time point DSC perfusion MRI (19 patients). Both groups included conventional MRI studies prior and after each perfusion MRI. This study design aimed to replicate decision making in clinical practice including multiple previous studies for each patient. SVM training was performed with all available MRI studies for each group and classification was based on different feature datasets from a single or multiple (subtracted features) time points. Classification accuracy comparisons were performed by calculating prediction error rates for different feature datasets and different time point analyses.
RESULTS:
Our results indicate that the addition of multiple time point perfusion MRI combined with structural (conventional with gadolinium-enhanced sequences) MRI features results in optimal classification performance (median error rate: 0.016, lowest value dispersion). Subtracted feature datasets improved classification performance, more prominently when the final and first perfusion studies were included in the analysis. On the contrary, in the single time point group analysis, structural feature-based classification performed best (median error rate: 0.012).
CONCLUSIONS:
Validation of our results with a larger patient cohort may have significant clinical importance in optimising imaging surveillance and clinical decision making for patients with HGG.

Multi-Parametric MRI as Supplement to mRANO Criteria for Response Assessment to MDNA55 in Adults with Recurrent or Progressive Glioblastoma.

Copyright © 2019 by American Society of Clinical Oncology
Journal of Clinical Oncology. 2019 May;37(15)_suppl doi: 10.1200/JCO.2019.37.15_suppl.e13559

Abstract

BACKGROUND:
Modified response assessment in neuro-oncology (mRANO) criteria are widely used in GBM but seem insufficient to capture Pseudoprogression (PsP), which occurs due to extensive inflammatory infiltration, increased vascular permeability, tumor necrosis and edema. mRANO criteria recommend volumetric response evaluation using contrast-enhanced T1 subtraction maps for identifying PsP. Our approach incorporates multi-parametric MRI biomarkers to unravel the true PsP from recurrence or distinguish Pseudo Response (PsR) – following anti-VEGF agents – from delayed (immuno)response.
METHODS:
Multiple time-points MRI (18-24h after convection-enhance delivery of the anti-IL4-R agent MDNA55, then at 30-day intervals) was utilized to determine response. Multi-parametric MRI biomarkers analyzed included (1) 3D-FLAIR-T2-based tumor volume assessment reflecting edema, necrosis and tumor infiltration; (2) 3D-gadolinium-enhanced-based tumor volume estimation reflecting active tumor infiltration, neo-angiogenesis and disrupted blood brain barrier; (3) Dynamic susceptibility contrast-based relative cerebral blood volume (rCBV) measurements for estimation of the vascular tumour properties; and (4) Diffusion weighted imaging – Apparent diffusion coefficient measurements that assess interstitial edema, tumor cellularity and ischemic injury.
RESULTS:
We demonstrate similar imaging phenotypes on conventional FLAIR-T2- and enhanced T1- MR images among different disease states (PsP vs true progression, PsR vs and immuno-response) and describe the perfusion and diffusion MRI biomarkers that improve response staging including PsP masking true progression, PsP masking clinical response, early progression with delayed response, and differentiation between true and PsR. The results are compared with the mRANO-based assessments for concurrence.
CONCLUSIONS:
Incorporating multi-parametric MRI measurements to determine the complex underlying tissue processes enables a better assessment of PsP, PsR and delayed tumour response, and can supplement mRANO-based response assessments in GBM patients undergoing novel immunotherapies.

Early Response Assessment Through Multiparametric MRI Based Endpoints In A Phase II Multicenter Study Evaluating the Efficacy of DPX-Survivac, Intermittent Low Dose Cyclophosphamide (CPA) and Pembrolizumab Combination Study in Subjects with Solid Tumors.

Copyright © 2019 by American Society of Clinical Oncology
Journal of Clinical Oncology. 2019 May;37(15)_suppl doi: 10.1200/JCO.2019.37.15_suppl.e14245

Abstract

BACKGROUND:
Accurate assessment of tumor response to immunotherapy is challenged by pseudoprogression that mimics true progression. Conventional imaging and RECIST assessment do not adequately distinguish between them given their inability to account for changes in the tumor microenvironment. DPX-Survivac is a novel T cell activating therapy that triggers immune responses against tumors expressing survivin and is being studied in this trial in combination with CPA and pembrolizumab in several solid tumors. Multiparametric MRI approaches – dynamic contrast-enhanced MRI and diffusion-weighted imaging MRI are useful for accurate assessment of structural, perfusion and vascular assessment of the lesion and may identify pseudoprogression and compare to the RECIST-based assessment.
METHODS:
The study will enroll up to 226 evaluable subjects in 5 different cohorts: ovarian cancer, HCC, NSCLC, bladder cancer and MSI-H cancer. These subjects will undergo initial imaging 28 days prior to treatment, to be assessed based on RECIST 1.1, and a pre-treatment tumor biopsy for quantitation of survivin and PD-L1 expression and MSI analyses. Treatment for 35 cycles or until disease progression. All patients will have CT images for RECIST 1.1 and iRECIST assessment. A subset of subjects will undergo mpMRI to calculate advanced imaging biomarkers.
RESULTS:
MRI, clinical and patient-reported outcomes will be analyzed.
CONCLUSIONS:
This study will provide important evidence on the utility of mpMRI + CT-based assessment of response to immunotherapy and use it as an adjunct to the CT-based RECIST criteria by providing insight on how tumor lesions are impacted by treatment.

Radiomics in Clinical Trials – The Rationale, Current Practices, and Future Considerations

Radiomics involves deep quantitative analysis of radiological images for structural and/or functional information. – It is a phenomic assessment of disease to understand lesion microstructure, microenvironment and molecular/cellular function. – In oncology, it helps us accurately classify, stratify and prognosticate tumors based on if, how and when they transform, infiltrate, involute or metastasize, – Utilizing radiomics in clinical trials is exploratory, and not an established end-point. – Integrating radiomics in an imaging-based clinical trials involves a streamlined workflow to handle large datasets, robust platforms to accommodate machine learning calculations, and seamless incorporation of derived insights into outcomes matrix.

LIVER CANCER

LIVER CANCER

Liver imaging in patients with a history of known or suspected malignancy is important because the liver is a common site of metastatic spread, especially tumours from the colon, lung, pancreas and stomach, and in patients with chronic liver disease who are at risk for developing hepatocellular carcinoma.

The goal of liver imaging in oncologic patients includes liver tumour detection and characterisation.

Liver biopsy is a standard procedure for diagnosing and staging non-alcoholic fatty-liver disease. However, biopsy has a number of disadvantages, including sampling error, intra- and inter-rater variability and poor patient acceptance due to potential complications. Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death worldwide.

Quantitative imaging is a promising approach for evaluating normal biological or pathogenic processes, as well as response to treatment and intervention without the need for invasive procedures. The field of hepatic MRI is relatively new compared with other organ systems as the complexity of hepatic imaging, including the dual blood supply of the portal vein and hepatic vessels, makes tracer imaging particularly challenging.

HCC commonly develops in the setting of concomitant or previous cirrhotic nodules or hepatic fibrosis, which can complicate classification of suspicious lesions using conventional imaging techniques alone. Depending on the imaging technique used (MRI, CT, US), various quantitative biomarkers can be extracted.

Proton Density Fat Fraction (PDFF)

Chemical-shift imaging is used to separate the liver signal into its fat and water parts.  PDFF is the fraction of MRI-visible protons attributable to fat divided by all protons in the liver attributable to fat and water. This technique acquires GREs  at appropriately spaced echo times. To increase examination accuracy, a low flip angle is used for minimizing T1 bias, along with multiple echoes for correcting T2* effects.

A standard MRI scanner of major manufactures will allow to reproducibly image patients and interpret images. The diagnostic accuracy of MRI-PDFF was validated by several studies, including Idilman et al and Bannas et al, which showed that MRI-based PDFF assessments closely correlated with histology acquired from liver biopsy.

MR Spectroscopy 

MR Spectroscopy, similarly to liver biopsy, is collected from a single region positioned in the liver parenchyma and directly measures the differences in water and fat peaks on a resonance frequency domain. Clearly, the quantification is done only within a particular region which limits the comprehensive understanding of the liver disease. Currently, MRS is not available on all MR scanners, which confines its routine adoption for clinical trials and clinical practice.

MR Electrography 

MR elastography uses a modified phase-contrast pulse sequence to visualize rapidly propagating mechanical shear waves. It is available in MRI scanners by major manufactures and can be acquired simultaneously with  other MRI sequences.  The best known application of MRE is in quantification of hepatic fibrosis.

Dynamic Contract Enhanced MRI

Currently, the use of DCE-MRI in both primary hepatic tumors and metastatic lesions are being investigated, as well as non-oncologic disease processes including hepatocellular fibrosis, cirrhosis, acute liver failure, and post-liver transplant rejection. DCE-MRI and tracer parameters can help differentiate regenerative, benign nodules from more concerning lesions. Sahani et al. reported models that differentiated blood flow and blood volume found differences in moderately or poorly differentiated HCCs. General concepts of differing microvascular structure and selection of appropriate anti-angiogenic therapies are important to maximize treatment response. DCE-MRI may assist in assessment of tumor response even before RECIST criteria can apply; early changes during therapy as demonstrated by perfusion parameters are not often seen on morphologic methods of imaging. Our upcoming publication will detail this further.

Our experience in liver imaging and quantitative biomarkers suggests that while there are multiple challenges in implementing MRI-PDFF, MRS, MRE, DCE-MRI in clinical trials, these techniques are reliable and noninvasive tools to assess hepatic steatosis, fibrosis and microvascular structure.In lights of sophisticated therapeutic developments and high treatment standards, we anticipate that in the next few years  multiparametric MR imaging will become more of a standard, enabling better diagnosis and faster assessments of the treatment response.

References

  • Sommer, W. H. et al. Contrast agents as a biological marker in magnetic resonance imaging of the liver: conventional and new approaches. Abdom. Imaging 37, 164–79 (2012).Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer45, 228–247. (2009)
  • Yang, X. & Knopp, M. V. Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol2011, 732848 (2011).
  • Li, S. P. & Padhani, A. R. Tumor response assessments with diffusion and perfusion MRI. J. Magn. Reson. Imaging35, 745–63 (2012).
  • Sourbron, S. P. & Buckley, D. L. Classic models for dynamic contrast-enhanced MRI. NMR Biomed.26(8), 1004–27 (2013).
  • Hylton, N. Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker. J. Clin. Oncol.24, 3293–8 (2006).
  • Ferl, G. Z. & Port, R. E. Quantification of antiangiogenic and antivascular drug activity by kinetic analysis of DCE-MRI data. Clin. Pharmacol. Ther.92, 118–24 (2012).
  • Knopp, M., Giesel, F., Marcos, H., von Tengg-Kobligk, H. & Choyke, P. Dynamic contrast-enhanced magnetic resonance imaging in oncology. Top Magn Resnon Imaging12, 301–8. (2001).
  • Padhani, A. & Husband, J. Dynamic contrast-enhanced MRI studies in oncology with an emphasis on quantification, validation and human studies. Clin Radiol56, 607–20 (2001).
  • Padhani, A. Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging16, 407–22 (2002).
  • Tofts, P. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging7, 91–101 (1997).
  • Barral, J. et al. A robust methodology for in vivo T1 mapping. Magn Reson Med64, 1057–67 (2010).
  •  DCE MRI Technical Committee. DCE MRI Quantification Profile, Quantitative Imaging Biomarkers Alliance. Version 1.0. Publicly Reviewed Version. Available from: RSNA.ORG/QIBA
  • O’Connor, J., Jackson, A., Parker, G., Roberts, C. & Jayson, G. Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol9, 167–77 (2012).
  • Boesen, M. et al. Automatic Computer Aided Quantification Of Synovitis In Rheumatoid Arthritis Using Dynamic MRI And The Impact Of Movement Correction On Signal To Noise Ratio (SNR) And Region Of Interest (ROI) Analysis [abstract]. Arthritis Rheum60, 773 (2009).
  • Kubassova, O. et al. in Med. Image Comput. Comput. Interv. – MICCAI. Lect. Notes Comput. Sci. Vol. 4792 261–269 (2007).
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Experience: Scoring Systems
  • LI-RADS
  • RECIST1.1
  • iRECIST
Experience: Imaging
  • US
  • CT
  • MRI
  • PET
Publications

Since 2007, over 2000 articles were published to cover scientific discoveries, technology break-throughs and special cases. We list here some critically important papers and abstracts.

Testimonials

Combining our technologies and business advisory services with promising life science companies has yielded spectacular results over the past five years. As a trusted partner to many biotech and pharma companies, IAG’s team is proud to share your words and quotes.

PROSTATE CANCER

PROSTATE CANCER

Standardised image acquisition and comprehensive automated analysis of multi-parametric Magnetic Resonance Imaging (mpMRI) are crucial in prostate cancer for optimal diagnosis and therapy and improving patient outcome. There is a clear need for computer-aided analysis and for software which can support clinical workflow to enable faster, more quality-controlled analysis and the extraction of both functional and anatomical information on suspicious lesions.

Prostate cancer (PCa) is the second most common cancer in men worldwide. The serum level of prostate-specific antigen (PSA) has been widely used for screening since the early 1990s. If after digital rectal examination, a cancer is suspected a biopsy is required. PSA, however, remains a controversial method of choice as it has poor sensitivity and an unacceptably low specificity. In addition, the low-cost 12-core trans-rectal ultrasound (TRUS)-guided biopsy routinely misses and under-stages cancers. As a consequence, too many patients are still being unnecessarily treated for indolent cancers.

Technological advances in MR sequences over the last few decades have resulted in significant improvements in MRI so that it is now a pivotal modality in prostate cancer management. Multiparametric Magnetic Resonance Imaging (mpMRI) combines anatomical images from T2-weighted imaging (T2wI) with functional sequences:

  • diffusion-weighted imaging (DWI), which quantifies the microscopic mobility of water molecules in tissues, and the apparent-diffusion coefficient (ADC) derived from it.
  • dynamic contrast-enhanced (DCE) MRI, which is based on the permeability of blood vessels and extravasation of contrast agent into adjacent tissue.

While high-resolution T2wI provides the best assessment of the prostate’s morphology, margins, and internal structure, DWI brings specificity, and DCE adds sensitivity, together making mpMRI especially effective in revealing anterior prostate cancer in men with negative random TRUS-biopsy.

MpMRI offers considerable information on prostatic lesions including the localisation,

The Dynamika software allows the computation of an ADC map from a DWI dataset if it is not already provided within the study. Furthermore, Regions-Of-Interest (ROIs) defined in a view are instantaneously propagated, mapped, and can be modified in the other views, with the relevant pixel value statistics being available.
Optimising (Structured) Reporting and Communication of Findings. The benefits of proforma type reporting have been widely acknowledged, and the PiRADS standardised graphic prostate scheme and scores aim to harmonise the reading and communication of the findings. Nevertheless, assessing multiple lesions by mapping subjective interpretations of different images into a standardised score and localising each one on pre-defined prostatic regions, requires both a high level of expertise and time.

The introduction of interactive PiRADS report forms in the Dynamika software allows readers to experience a direct connection between the images and the reporting sheet. Images, ROIs and reports are no longer detached, and instant feedback in the report can serve a better understanding and analysis of the images.

Not only are the scans within a study synchronized spatially, but Dynamika also links manipulations such as zoom and pan to retain a coherent presentation of information between the MR sequences. Upon review, the user can open a window of the interactive PiRADS form, in which the synchronised ROIs are listed to be assigned to a specific sector defined by the standardised prostatic regions diagram.

The PiRADS form is regularly updated with the most recent research updates in line with PIRADS v1 and v2 and include PSA, prostate and lesion volume, biopsy results, Gleason score, extracapsular extension, seminal vesicular invasion, lesion scores, DWI results, and 2D visualisations. In addition, the customisable user individual interface allows for adjustments for both PI-RADS updates as well as for user-specific preferences. Advances in computer aided-diagnosis (CADx) could offer decreased reading time and consistent risk assessment of cancer presence. Evaluation of the principal current CADx systems for “Prostate Cancer Diagnosis” has unfortunately shown that they are not fully ready yet. Improvements will be made over the next decade and the wide deployment of prostate CADx systems in the clinical environment will eventually occur.

In the meantime, more focused applications for suspicious lesion detection, localisation and description, based on the combination of T2wI, DWI and DCE, could help readers efficiently grade and report lesions in PiRADS form. This is an area in which the company Image Analysis, developers of the Dynamika software package, is actively involved. Enabling Advanced Quantitative Analysis. Advanced quantitative analysis and colour maps based on DCE (parametric maps, pharmacokinetic parameters, subtraction) are also available and novel methods developed either by in-house research, collaborations, or external innovators – are continually integrated into the Dynamika software to aid lesion classification. These quantitative outcomes may be linked to PiRADS scores for lesions and therefore allow a more precise monitoring for a specific lesion. Facilitating Disease and Treatment Monitoring. The ability to quantify ‘evolution’ over time is key in active surveillance or treatment monitoring. Imaging biomarkers are used to categorise lesions, measure disease progression or estimate doses for focal radiotherapy as well as guide biopsies. Multiple images of multiple time series need to be compared. This is difficult from a viewing perspective as well as from a timing aspect. The time-consuming task of comparison of a current image with a prior is automated. All datasets and reports are stored in one central database, easily and rapidly accessible from any computer connected to the internet. Further, the software organises the arrangement of the individual scans in an intuitive way and allows easy identification and propagation of ROIs between scans and studies.

MpMRI is growing in many areas of PCa management, as a result of the constant efforts of clinical research communities to promulgate and refine standards for acquisition, interpretation and reporting. The value and richness of the multi-parametric approach is undeniable in oncology, and mpMRI offers hopes and opportunities to further expand the understanding of PCa mechanisms, provide better detection and staging of lesions, evaluate therapeutic effects more efficiently and deliver more focused and personalised treatments.

Technology needs to adapt and offer better, easier and faster ways to present and analyse ever-larger and more complex clinical data. In the current era of evidence-based medicine, technology also needs to provide platforms to allow multi-disciplinary and multi-site/national collaborative research to investigate, evaluate and validate new treatments, quantitative imaging markers, or computer aided-diagnosis tools for PCa. Dynamika is such a dedicated cloud platform [9] developed for and in collaboration with researchers, radiologists and urologists. By incorporating the latest standards and keeping abreast of science, Image Analysis aims at improving quality and efficiency, and ensuring reliability and reproducibility of PCa diagnosis and treatment from more and more complex mpMRI data.

REFERENCES
1. Andriole GL Prostate cancer screening in the randomized prostate, lung, colorectal,
and ovarian cancer screening trial: mortality results after 13 years of follow-up,. J Natl
Cancer Inst. 2012; 104: 125
2. Serefoglu EC How reliable is 12-core prostate biopsy procedure in the detection of
prostate cancer?”J Can Urol Ass 2013; 5: E293–E298,
3. Komai Y High diagnostic ability of multiparametricmagnetic resonance imaging
to detect anterior prostate cancer missed by transrectal 12-core biopsy. J Urology, 2013;
190: 867
4. Turkbey B Prostate cancer: value of multiparametric MR imaging at 3 T for detection—
histopathologic correlation. Radiology 2010; 255 : 89.
5. Barentsz JO & Richenberg ESUR prostate MR guidelines, J Eur Radiol. 2012; 22: 746.
6. American College of Radiology. PI-RADSProstate Imaging-Reporting and Data System,
2015 version 2, Reston, VA, USA; 2015. http://www.acr.org/~/media/ACR/Documents/PDF/
QualitySafety/Resources/PIRADS/PIRADS%20V2.pdf
7. Image Analysis, MRI analysis platform Dynamika http://www.imageanalysis.org.uk/
8. Wang S et al. Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A
Technical Review of Current Research. Biomed Res Int. 2014; 789561.
9. Kubassova O “Scaling computer aided detection from workstation to the cloud: medical
image analysis today”, Diagnostic Imaging Europe, Oct 2014

Guidelines on staging and characterization of prostate cancer have been published by the National Comprehensive Cancer Network. http://emedicine.medscape.com/article/379996-overview

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Experience: Scoring Systems
  • PI-RADS
  • RECIST 1.1
  • irRC (Immune-Related Response Criteria)
  • iRECIST
  • mRECIST
  • PET-based Standard Uptake Value (SUV)
  • Volumetric Assessment
Experience: Imaging
  • CT
  • anatomical MRI
  • perfusion imaging (DCE-MRI, DSC)
  • diffusion imaging (ADC, DWI, DTI)
  • Multiparametric MRI (mpMRI)
  • PET/CT
  • Multiparametric ultrasound (mpUS)
Publications

Since 2007, over 2000 articles were published to cover scientific discoveries, technology break-throughs and special cases. We list here some critically important papers and abstracts.

Testimonials

Combining our technologies and business advisory services with promising life science companies has yielded spectacular results over the past five years. As a trusted partner to many biotech and pharma companies, IAG’s team is proud to share your words and quotes.