Jamie Franklin

MRI features of the complete histopathological response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy

AIM To describe the post-chemoradiotherapy magnetic resonance imaging (MRI) features of locally advanced rectal carcinoma (LARC) in which there has been a complete histopathological response to neoadjuvant chemoradiotherapy (CRT). MATERIALS AND METHODS This retrospective cohort study was performed between January 2005 and November 2009 at a regional cancer centre. Consecutive patients with LARC and a histopathological complete response to long-course CRT were identified. Pre- and post-treatment MRI images were reviewed using a proforma for predefined features and response criteria. ymrT0 was defined as the absence of residual abnormality on MRI. RESULTS Twenty patients were included in the study. Seven (35%) ypT0 tumours were ymrT0. All 13 ypT0 tumours not achieving ymrT0 appearances had a good radiological response, with at least 65% tumour reduction. The appearances were heterogeneous: in 11/13 patients the tumour was replaced by a region of at least 50% low signal on MRI, with 8/13 having ≥80% low signal, and 3/13 with 100% low signal. CONCLUSION MRI may be useful in identifying a complete histopathological response. However, the MRI appearances of ypT0 tumours are heterogeneous and conventional MRI complete response criteria will not detect the majority of patients with a complete histopathological response.

Clinical Trial of Oral Nelfinavir before and during Radiation Therapy for Advanced Rectal Cancer.

Purpose: Nelfinavir, a PI3K pathway inhibitor, is a radiosensitizer that increases tumor blood flow in preclinical models. We conducted an early-phase study to demonstrate the safety of nelfinavir combined with hypofractionated radiotherapy (RT) and to develop biomarkers of tumor perfusion and radiosensitization for this combinatorial approach. Experimental Design: Ten patients with T3-4 N0-2 M1 rectal cancer received 7 days of oral nelfinavir (1,250 mg b.i.d.) and a further 7 days of nelfinavir during pelvic RT (25 Gy/5 fractions/7 days). Perfusion CT (p-CT) and DCE-MRI scans were performed pretreatment, after 7 days of nelfinavir and prior to the last fraction of RT. Biopsies taken pretreatment and 7 days after the last fraction of RT were analyzed for tumor cell density (TCD). Results: There were 3 drug-related grade 3 adverse events: diarrhea, rash, and lymphopenia. On DCE-MRI, there was a mean 42% increase in median Ktrans, and a corresponding median 30% increase in mean blood flow on p-CT during RT in combination with nelfinavir. Median TCD decreased from 24.3% at baseline to 9.2% in biopsies taken 7 days after RT (P = 0.01). Overall, 5 of 9 evaluable patients exhibited good tumor regression on MRI assessed by tumor regression grade (mrTRG). Conclusions: This is the first study to evaluate nelfinavir in combination with RT without concurrent chemotherapy. It has shown that nelfinavir-RT is well tolerated and is associated with increased blood flow to rectal tumors. The efficacy of nelfinavir-RT versus RT alone merits clinical evaluation, including measurement of tumor blood flow. Clin Cancer Res; 22(8); 1922–31. ©2016 AACR. See related commentary by Meyn et al., p. 1834

Liver Motion Estimation via Locally Adaptive Over-Segmentation Regularization

Despite significant advances in the development of deformable registration methods, motion correction of deformable organs such as the liver remain a challenging task. This is due to not only low contrast in liver imaging, but also due to the particularly complex motion between scans primarily owing to patient breathing. In this paper, we address abdominal motion estimation using a novel regularization model that is advancing the state-of-the-art in liver registration in terms of accuracy. We propose a novel regularization of the deformation field based on spatially adaptive over-segmentation, to better model the physiological motion of the abdomen. Our quantitative analysis of abdominal Computed Tomography and dynamic contrast-enhanced Magnetic Resonance Imaging scans show a significant improvement over the state-of-the-art Demons approaches. This work also demonstrates the feasibility of segmentation-free registration between clinical scans that can inherently preserve sliding motion at the lung and liver boundary interfaces.

Automated Colorectal Tumour Segmentation in DCE-MRI Using Supervoxel Neighbourhood Contrast Characteristics

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful protocol for assessing tumour progression from changes in tissue contrast enhancement. Manual colorectal tumour delineation is a challenging and time consuming task due to the complex enhancement patterns in the 4D sequence. There is a need for a consistent approach to colorectal tumour segmentation in DCE-MRI and we propose a novel method based on detection of the tumour from signal enhancement characteristics of homogeneous tumour subregions and their neighbourhoods. Our method successfully detected 20 of 23 cases with a mean Dice score of 0.68 +/- 0.15 compared to expert annotations, which is not significantly different from expert inter-rater variability of 0.73 +/- 0.13 and 0.77 +/- 0.10. In comparison, a standard DCE-MRI tumour segmentation technique, fuzzy c-means, obtained a Dice score of 0.28 +/- 0.17.

The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data.

A comprehensive framework for predicting response to therapy on the basis of heterogeneity in dceMRI parameter maps is presented. A motion-correction method for dceMRI sequences is extended to incorporate uncertainties in the pharmacokinetic parameter maps using a variational Bayes framework. Simple measures of heterogeneity (with and without uncertainty) in parameter maps for colorectal cancer tumours imaged before therapy are computed, and tested for their ability to distinguish between responders and non-responders to therapy. The statistical analysis demonstrates the importance of using the spatial distribution of parameters, and their uncertainties, when computing heterogeneity measures and using them to predict response on the basis of the pre-therapy scan. The results also demonstrate the benefits of using the ratio of Ktrans with the bolus arrival time as a biomarker.

Personalised Estimation of the Arterial Input Function for Improved Pharmacokinetic Modelling of Colorectal Cancer Using dceMRI

dceMRI is becoming a key modality for tumour characterisation and monitoring of response to therapy, because of the ability to identify the underlying tumour physiology. Pharmacokinetic PK models relate the contrast enhancement seen in dceMRI to physiological parameters but require accurate measurement of the AIF, the time-dependant contrast concentration in blood plasma. In this study, a novel method is introduced that overcomes the challenges of direct AIF measurement, by automatically estimating the AIF from the tumour tissue. This approach was evaluated on synthetic data 10% noise and achieved a relative error in K trans and k ep of 11.8 ±3.5% and 25.7 ±4.7 %, respectively, compared to 41 ±15 % and 60 ±32 % using a population model. The method improved the fit of the PK model to clinical colorectal cancer cases, was stable for independent regions in the tumour, and showed improved localisation of the PK parameters. This demonstrates that personalised AIF estimation can lead to more accurate PK modelling.

pCT derived arterial input function for improved pharmacokinetic analysis of longitudinal dceMRI for colorectal cancer

Dynamic contrast-enhanced MRI is a dynamic imaging technique that is now widely used for cancer imaging. Changes in tumour microvasculature are typically quantified by pharmacokinetic modelling of the contrast uptake curves. Reliable pharmacokinetic parameter estimation depends on the measurement of the arterial input function, which can be obtained from arterial blood sampling, or extracted from the image data directly. However, arterial blood sampling poses additional risks to the patient, and extracting the input function from MR intensities is not reliable. In this work, we propose to compute a perfusion CT based arterial input function, which is then employed for dynamic contrast enhanced MRI pharmacokinetic parameter estimation. Here, parameter estimation is performed simultaneously with intra-sequence motion correction by using nonlinear image registration. Ktrans maps obtained with this approach were compared with those obtained using a population averaged arterial input function, i.e. Orton. The dataset comprised 5 rectal cancer patients, who had been imaged with both perfusion CT and dynamic contrast enhanced MRI, before and after the administration of a radiosensitising drug. Ktrans distributions pre and post therapy were computed using both the perfusion CT and the Orton arterial input function. Perfusion CT derived arterial input functions can be used for pharmacokinetic modelling of dynamic contrast enhanced MRI data, when perfusion CT images of the same patients are available. Compared to the Orton model, perfusion CT functions have the potential to give a more accurate separation between responders and non-responders.

Management of a thyroid "incidentaloma" in a patient with cancer: a case report.

The routine use of 18F-fluorodeoxyglucose-positron emission tomography (PET)/computed tomography scans for staging and assessment of treatment response for cancer has resulted in a large number of thyroid abnormalities being detected as incidental findings (“incidentalomas”). Since most PET/CT scans are performed in the setting of a known nonthyroid malignancy, the need for “incidentalomas” to be further investigated and managed depends on the stage, prognosis, and current treatment plan for the known malignancy. We present a case describing the management of an incidental F-fluorodeoxyglucose-avid thyroid nodule detected in a patient with known metastatic colorectal cancer. On the basis of this case, we discuss the management of incidental PET-detected thyroid nodules in patients with metastatic cancer. Thyroid “incidentalomas” must be seen in the context of the prognosis and treatment plan for the known malignancy.

A Semi-automated Toolkit for Analysis of Liver Cancer Treatment Response Using Perfusion CT

Delineation of hepatic tumours is challenging in CT due to limited inherent tissue contrast, leading to significant intra-/inter-observer variability. Perfusion CT (pCT) allows quantitative assessment of enhancement patterns in normal and abnormal liver. This study aims to develop a semi-automated perfusion analysis toolkit that classifies hepatic tissue based on perfusion-derived parameters. pCT data from patients with hepatic metastases were used in this study. Tumour motion was minimized through image registration; perfusion parameters were derived and then employed in the training of a machine learning algorithm used to classify hepatic tissue. This method was found to deliver promising results for 10 data sets, with recorded sensitivity and specificity of the tissue classification in the ranges of 0.92–0.99 and 0.98–0.99 respectively. This semi-automated method could be used to analyze response over the treatment course, as it is not based on intensity values.