Neoplasms

Urinary Tract, Sexual Organs, and Pregnancy Conditions

All Conditions

Endometrial Neoplasms

Uterine Neoplasms

Genital Neoplasms, Female

Urogenital Neoplasms

Uterine Diseases

Genital Diseases, Female

Genital Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

All patients with confirmed endometrial cancer will have received Ultrasound and MRI as part of routine standard of care. Textural features from this will be analysed to see if can predict prognosis.

Radiomics: Analysis of textural features of imaging (MRI and Ultrasound)

Diana Marcus, MB BS

Sadaf Ghaem-Maghami

Andrea Rockall

Nishat Bharwani

Aim: Assess the value of MRI features in predicting prognosis in patients with endometrial cancer

This study will examine the MRI features of women with confirmed endometrial cancer to see if textural features can prognosticate patients.

Aim: Assess the value of MRI features in predicting prognosis in patients with endometrial cancer

Background:

Endometrial cancer is the commonest gynaecological cancer in the United Kingdom. MR Imaging is used to provide pre-treatment local staging in Endometrial Cancer based on the FIGO staging system. This MRI stage is used by clinicians, in combination with pre-operative histology and clinical assessment of the patient, to counsel patients about their prognosis.

Currently the information obtained from the MRI study is largely anatomical and concerns the extent of local tumour spread at the time of diagnosis. Each stage under the FIGO system is associated with a prognosis based on data from the literature. Important prognostic indicators include the depth of tumour invasion in the uterine muscle (myometrium), invasion of the cervical stroma by tumour, spread to lymph node tissues and the histological sub-type of tumour present.

However, with ever improving MRI technology there are now newer sequences employed as part of the staging study which may potentially yield more information. In other cancer types, e.g. prostate, breast and liver, textural analysis of the tumour on pre-treatment imaging (CT/PET-CT/MRI) is being analysed to try and identify features which could be used as prognostic markers for patient outcome.

The Study:

All patients with confirmed cancer on histology will receive an Ultrasound and MRI as part of routine care. The MRI scan will include T1, T2, DWI and contrast enhanced sequences. Patients referred to Hammersmith Hospital from other trusts who are diagnosed with endometrial cancer will be approached for consent and enrolment into latter part of study. These patients will have had an MRI but may not have had all the sequences needed and may therefore require a further MRI scan with additional sequences. These images will be saved and analysed. Pathological findings (e.g. type and grade of cancer) will be correlated with the radiological results.

Definitive management of endometrial cancer includes a hysterectomy for most patients. Following surgery patients will continue with routine standard of care and follow up. Patients will be followed up for 5 years to provide 5 year survival data. Images from ultrasound and MRI pelvis will be analysed to see if they can predict the 5 year survival.

This prospective study will also be used to validate a retrospective model using MRI textural features in endometrial cancer patients.

Can Staging Magnetic Resonance Imaging (MRI) Features Prognosticate Patients Presenting With Endometrial Cancer?

Assess features of pelvic MRIs (including (i) tumour image intensity, (II) shape, (III) texture and (IV) multiscale wavelet) and create a mathematical algorithm to predict prognostic outcome (5 year survival).

Assess textural features of pelvic MRI in conjunction with histopathology (type of tumour, grade LVSI, 'aggressiveness' of cancer

Imperial College London

Digestive System Diseases

Carcinoma

Carcinoma, Hepatocellular

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Adenocarcinoma

Liver Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Liver Diseases

Antineoplastic Agents

All Drugs and Chemicals

Hematinics

Amino Acids

Immune Checkpoint Inhibitors

Liver Extracts

Tyrosine Kinase Inhibitors

Tyrosine

Surgery

Y-90 radioembolization therapy is the main treatment

Radiation therapy

Examples include tyrosine kinase inhibitors and immune checkpoint inhibitors

Drug treatment

Pierce Chow

This project is a pilot study to interrogate the potential of LMS as a predictive tool for the selection of therapy for HCC patients. The reliability of LMS to predict patients' response following HCC therapy will leverage on an algorithm that is built from the pool of MRI scans from HCC patients pre- and post-treatment. In the study, MRI scans of 30 HCC and metastatic colorectal cancer (CM) patients (ratio of 4:1) will be analysed. CM cancer patients include patients whose cancers metastasized from colorectal cancer or primary liver cancer. These patients will either receive one of the treatment, surgical resection, Y90 or systemic therapy. A total of 4 MRI scans will be taken for each patient; the first MRI scan will be taken within a month before treatment initiation and the remaining MRI scans will be taken at the 1st, 3rd and 9th month post-initiation of treatment.

Liver cancer is currently the second most common cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers. There has been a marked increase in HCC-related annual death rates over the past two decades, with the majority of all cases of HCC worldwide found in the Asia-Pacific region. As such, HCC represents a major public health problem in the Asia-Pacific region and globally. There are a significant number of variables known to influence the prognosis of HCC, with the stage of underlying liver disease being a dominant factor. Elucidating these variables remains a challenge and the poor understanding of these variables has translated to poor prognostication of treatment outcomes. To date, no ideal predictive modality has been developed. One of the treatment therapies in HCC is surgical resection. Despite advances in the surgical and perioperative fields, potential posthepatectomy liver failure (PHLF) persists as a life-threatening complication, and is reported in up to 15% of patients (Aliza et al., 2017). This accentuates the need to develop accurate methods to quantitatively characterize future liver remnants (FLR) prior to surgery, as postoperative outcomes mainly depend on the size and quality of FLR (Cieslak et al., 2014).

This study seeks to address this unmet need as we will employ the LMS platform to assess and monitor changes in the liver health of HCC patients following treatment. In addition, this platform will be leveraged upon to monitor patients' response to the different treatment modalities such as surgical resection, Y90 and systemic therapy. These aims will be achieved through the quantitative characterization of liver tissues, which forms the basis of LMS technology. LMS is an MRI-based non-invasive tool that has attained CE marking and FDA clearance to aid the diagnosis of patients with liver disease. This technology is highly sensitive to subtle differences in liver tissue composition and samples the entire liver quickly, rendering it an ideal platform for liver tissue characterisation. LMS uses MRI mapping techniques to characterize liver tissue at the cellular level, delivering the quantification of liver fat and correlates of fibroinflammation and iron load using proton density fat fraction (PDFF), T1 and T2\* maps, respectively. The modelling algorithms of this device correct the T1 map (cT1) for the confounding effect of iron overload. In addition, this platform has further refined the measurements for PDFF to be more accurate in liver tissue characterisation. Previously, the use of LMS to predict clinical outcomes through tissue characterisation has been validated in a few clinical studies on patients with liver disease. In a prospective general hepatology clinical cohort following 112 patients for a median of 27 months, patients with higher cT1 values tended to experience clinical events, whereas patients with normal or low cT1 values had no clinical events (Pavlides et al., 2016). Additionally, from a prospective study on 71 patients with suspected non-alcoholic fatty liver disease (NAFLD), cT1 values have been shown to correlated with cirrhosis, ballooning and significant NAFLD (Pavlides et al., 2016). Finally, an independent study has also demonstrated the utility of T1 mapping in predicting clinical outcomes and for distinguishing decompensated cirrhotics from compensated cirrhotics (Bradley, 2018).

In-depth characterisation of liver tissues will be done following Couinaud segmentation of the liver. Specific to the surgical resection cohort, estimated FLR volume and cT1 will be combined to refine the LMS platform in the assessment of liver health. The significance of this in the long term resides in more accurate assessment of surgical risks, which is paramount to improve the care of HCC patients considered for surgical resection. By adopting the LMS platform to monitor patients' response to treatment longitudinally through the characterisation of their liver tissues, our study also seeks to discern features present in the LMS platform that are predictive of patients' outcomes. Additionally, this study will compare anonymized blood test results and anonymized histological reports (histological reports only for the surgical resection cohort) against features observed in the LMS platform to better discern features indicative of patients' outcomes.

This study will monitor patients closely before treatment and after their treatment with follow-up visits, and the MRI scans of these patients across these visits will be analysed using the LMS. A total of 4 MRI scans will be taken for each patient with 1st MRI scan taken within 6 weeks before treatment and the remaining MRI scans taken at the 1st, 3rd and 9th month post-initiation of treatment.

LiverMultiScan Analysis of MRI Scans in HCC

The Utility of LiverMultiScan Analysis of MRI Scans in Patients With Hepatocellular Carcinoma for Improved Prognostication and as an Aid to the Selection of Therapy

The objective is to predict the clinical outcomes of patients undergoing treatment

The objective is to refine the prediction capabilities of these software or tools. The software used in this study is the LiverMultiScan (LMS) by Perspectum which will do post-analysis of patients' MRI scans.

Perspectum

National Cancer Centre, Singapore

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Can Staging Magnetic Resonance Imaging (MRI) Features Prognosticate Patients Presenting With Endometrial Cancer?	NCT03543215	Endometrial Can... Endometrial Neo...	Radiomics: Anal...	18 Years -	Imperial College London
Can Staging Magnetic Resonance Imaging (MRI) Features Prognosticate Patients Presenting With Endometrial Cancer?	NCT03543215	Endometrial Can... Endometrial Neo...	Radiomics: Anal...	18 Years -	Imperial College London
LiverMultiScan Analysis of MRI Scans in HCC	NCT04451603	Hepatocellular ...	Surgery Radiation thera... Drug treatment	21 Years - 90 Years	National Cancer Centre, Singapore

Spots Global Cancer Trial Database for prognostication