Evaluation of simultaneous multi-slice acquisition with advanced processing for free-breathing diffusion-weighted imaging in patients with liver metastasis.

OBJECTIVES: Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) acquisition and advanced processing can accelerate acquisition time and improve MR image quality. This study evaluated the image quality and apparent diffusion coefficient (ADC) measurements of free-breathing DWI acquired from patients with liver metastases using a prototype SMS-DWI acquisition (with/without an advanced processing option) and conventional DWI. METHODS: Four DWI schemes were compared in a pilot 5-patient cohort; three DWI schemes were further assessed in a 24-patient cohort. Two readers scored image quality of all b-value images and ADC maps across the three methods. ADC measurements were performed, for all three methods, in left and right liver parenchyma, spleen, and liver metastases. The Friedman non-parametric test (post-hoc Wilcoxon test with Bonferroni correction) was used to compare image quality scoring; t-test was used for ADC comparisons. RESULTS: SMS-DWI was faster (by 24%) than conventional DWI. Both readers scored the SMS-DWI with advanced processing as having the best image quality for highest b-value images (b750) and ADC maps; Cohen's kappa inter-reader agreement was 0.6 for b750 image and 0.56 for ADC maps. The prototype SMS-DWI sequence with advanced processing allowed a better visualization of the left lobe of the liver. ADC measured in liver parenchyma, spleen, and liver metastases using the SMS-DWI with advanced processing option showed lower values than those derived from the SMS-DWI method alone (t-test, p < 0.0001; p < 0.0001; p = 0.002). CONCLUSIONS: Free-breathing SMS-DWI with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients. CLINICAL RELEVANCE STATEMENT: Free-breathing simultaneous multi-slice- diffusion-weighted imaging (DWI) with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients. KEY POINTS: • Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) can accelerate acquisition time and improve image quality. • Apparent diffusion coefficients (ADC) measured in liver parenchyma, spleen, and liver metastases using the simultaneous multi-slice DWI with advanced processing were significantly lower than those derived from the simultaneous multi-slice DWI method alone. • Simultaneous multi-slice DWI sequence with inline advanced processing was faster and demonstrated better image quality in liver patients.

A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis.

BACKGROUND: Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront characterisation of the tumour is difficult, and under-grading is common. Radiomics has the potential to non-invasively characterise the so-called radiological phenotype of tumours. We aimed to develop and independently validate a CT-based radiomics classification model for the prediction of histological type and grade in retroperitoneal leiomyosarcoma and liposarcoma. METHODS: A retrospective discovery cohort was collated at our centre (Royal Marsden Hospital, London, UK) and an independent validation cohort comprising patients recruited in the phase 3 STRASS study of neoadjuvant radiotherapy in retroperitoneal sarcoma. Patients aged older than 18 years with confirmed primary leiomyosarcoma or liposarcoma proceeding to surgical resection with available contrast-enhanced CT scans were included. Using the discovery dataset, a CT-based radiomics workflow was developed, including manual delineation, sub-segmentation, feature extraction, and predictive model building. Separate probabilistic classifiers for the prediction of histological type and low versus intermediate or high grade tumour types were built and tested. Independent validation was then performed. The primary objective of the study was to develop radiomic classification models for the prediction of retroperitoneal leiomyosarcoma and liposarcoma type and histological grade. FINDINGS: 170 patients recruited between Oct 30, 2016, and Dec 23, 2020, were eligible in the discovery cohort and 89 patients recruited between Jan 18, 2012, and April 10, 2017, were eligible in the validation cohort. In the discovery cohort, the median age was 63 years (range 27-89), with 83 (49%) female and 87 (51%) male patients. In the validation cohort, median age was 59 years (range 33-77), with 46 (52%) female and 43 (48%) male patients. The highest performing model for the prediction of histological type had an area under the receiver operator curve (AUROC) of 0·928 on validation, based on a feature set of radiomics and approximate radiomic volume fraction. The highest performing model for the prediction of histological grade had an AUROC of 0·882 on validation, based on a radiomics feature set. INTERPRETATION: Our validated radiomics model can predict the histological type and grade of retroperitoneal sarcomas with excellent performance. This could have important implications for improving diagnosis and risk stratification in retroperitoneal sarcomas. FUNDING: Wellcome Trust, European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group, the National Institutes for Health, and the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research.

Radiomics in Oncology: A Practical Guide.

Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine. The authors provide a practical approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasks that involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival. The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation. Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest. To improve model performance and reduce overfitting, redundant and nonreproducible features are removed. Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artificial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn. Online supplemental material is available for this article. Published under a CC BY 4.0 license.

Probing structure of normal and malignant prostate tissue before and after radiation therapy with luminal water fraction and diffusion-weighted MRI.

BACKGROUND: Interpretation of diffusion in conjunction with T2 -weighted MRI is essential for assessing prostate cancer; however, the combination of apparent diffusion coefficient (ADC) with quantitative T2 mapping remains unexplored. PURPOSE: To document the T2 components and ADC of untreated and irradiated nonmalignant prostate tissue as a measure of their glandular luminal and cellular compartments and to compare values with those of tumor. STUDY TYPE: Prospective. POPULATION: Twenty-four men with prostate cancer (14 untreated; 10 with biochemical recurrence following radiation therapy). FIELD STRENGTH/SEQUENCES: Endorectal 3 T MRI including a 32-echo gradient echo and spin echo (GRASE) and an 8 b-value diffusion-weighted sequence. ASSESSMENT: Regions of interest were drawn on ADC maps and T2 -weighted images around focal lesions in areas of biopsy-positive prostate cancer and in nonmalignant areas of untreated and irradiated peripheral zone (PZ), and untreated transitional zone (TZ). Multiecho T2 data were fitted with mono-/biexponential decay and nonnegative least squares functions. The luminal water fraction (LWF) was derived. STATISTICAL TESTS: The preference between mono- and biexponential decay was assessed using the Bayesian information criterion. Differences in fitted parameters between tissue types were compared (paired t-test within groups, Kruskal-Wallis and Wilcoxon rank-sum test between groups) and correlations between ADC and T2 components assessed (Spearman rank correlation test). RESULTS: LWF in tumor (0.09) was significantly lower than in PZ or TZ (0.27 and 0.18, P < 0.01, respectively), but tumor values were comparable to nonmalignant irradiated prostate (0.08). The short T2 relaxation rate was lower in tumor than in nonmalignant untreated or irradiated tissue (significant compared with TZ, P = 0.01). There was a strong correlation between LWF and ADC in normal untreated tissue (r = 0.88, P < 0.001). This relationship was absent in nonmalignant irradiated prostrate (r = -0.35, P = 0.42) and in tumor (r = -0.04, P = 0.88). DATA CONCLUSION: T2 components in conjunction with ADC can be used to characterize untreated and irradiated nonmalignant prostate and tumor. LWF is most useful at discriminating tumor in the untreated prostate. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:619-627.

Characterisation of fibrosis in chemically-induced rat mammary carcinomas using multi-modal endogenous contrast MRI on a 1.5T clinical platform.

OBJECTIVES: To determine the ability of multi-parametric, endogenous contrast MRI to detect and quantify fibrosis in a chemically-induced rat model of mammary carcinoma. METHODS: Female Sprague-Dawley rats (n=18) were administered with N-methyl-N-nitrosourea; resulting mammary carcinomas underwent nine-b-value diffusion-weighted (DWI), ultrashort-echo (UTE) and magnetisation transfer (MT) magnetic resonance imaging (MRI) on a clinical 1.5T platform, and associated quantitative MR parameters were calculated. Excised tumours were histologically assessed for degree of necrosis, collagen, hypoxia and microvessel density. Significance level adjusted for multiple comparisons was p=0.0125. RESULTS: Significant correlations were found between MT parameters and degree of picrosirius red staining (r > 0.85, p < 0.0002 for ka and δ, r < -0.75, p < 0.001 for T1 and T1s, Pearson), indicating that MT is sensitive to collagen content in mammary carcinoma. Picrosirius red also correlated with the DWI parameter fD* (r=0.801, p=0.0004) and conventional gradient-echo T2* (r=-0.660, p=0.0055). Percentage necrosis correlated moderately with ultrashort/conventional-echo signal ratio (r=0.620, p=0.0105). Pimonidazole adduct (hypoxia) and CD31 (microvessel density) staining did not correlate with any MR parameter assessed. CONCLUSIONS: Magnetisation transfer MRI successfully detects collagen content in mammary carcinoma, supporting inclusion of MT imaging to identify fibrosis, a prognostic marker, in clinical breast MRI examinations. KEY POINTS: • Magnetisation transfer imaging is sensitive to collagen content in mammary carcinoma. • Magnetisation transfer imaging to detect fibrosis in mammary carcinoma fibrosis is feasible. • IVIM diffusion does not correlate with microvessel density in preclinical mammary carcinoma.

Extracranial Soft-Tissue Tumors: Repeatability of Apparent Diffusion Coefficient Estimates from Diffusion-weighted MR Imaging.

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI.

OBJECTIVES: Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours. METHODS: Forty-two patients (24 squamous cell carcinomas, 14 well/moderately differentiated, 10 poorly differentiated; 15 adenocarcinomas, 13 well/moderately differentiated, two poorly differentiated; three rare types) were imaged at 3 T using nine b-values (0 to 800 s mm-2). Mono-exponential, stretched exponential, kurtosis, statistical, and bi-exponential models were fitted. Model preference was assessed using Bayesian Information Criterion analysis. Differences in fitted parameters between tumour types/grades and correlation between fitted parameters were assessed using two-way analysis of variance and Pearson's linear correlation coefficient, respectively. RESULTS: Non-mono-exponential models were preferred by 83 % of tumours with bi-exponential and stretched exponential models preferred by the largest numbers of tumours. Apparent diffusion coefficient (ADC) and diffusion coefficients from non-mono-exponential models were significantly lower in poorly differentiated tumours than well/moderately differentiated tumours. α (stretched exponential), K (kurtosis), f and D* (bi-exponential) were significantly different between tumour types. Strong correlation was observed between ADC and diffusion coefficients from other models. CONCLUSIONS: Non-mono-exponential models were preferred to the mono-exponential model in DW-MRI data from cervical tumours. Parameters of non-mono-exponential models showed significant differences between types and grades of tumours. KEY POINTS: • Non-mono-exponential DW-MRI models are preferred in the majority of cervical tumours. • Poorly differentiated cervical tumours exhibit lower diffusion coefficients than well/moderately differentiated tumours. • Non-mono-exponential model parameters α, K, f, and D* differ between tumour types. • Micro-structural features are likely to affect parameters in non-mono-exponential models differently.

Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort.

OBJECTIVES: To examine repeatability of parameters derived from non-Gaussian diffusion models in data acquired in children with solid tumours. METHODS: Paediatric patients (<16 years, n = 17) were scanned twice, 24 h apart, using DWI (6 b-values, 0-1000 mm-2 s) at 1.5 T in a prospective study. Tumour ROIs were drawn (3 slices) and all data fitted using IVIM, stretched exponential, and kurtosis models; percentage coefficients of variation (CV) calculated for each parameter at all ROI histogram centiles, including the medians. RESULTS: The values for ADC, D, DDCα, α, and DDCK gave CV < 10 % down to the 5th centile, with sharp CV increases below 5th and above 95th centile. K, f, and D* showed increased CV (>30 %) over the histogram. ADC, D, DDCα, and DDCK were strongly correlated (ρ > 0.9), DDCα and α were not correlated (ρ = 0.083). CONCLUSION: Perfusion- and kurtosis-related parameters displayed larger, more variable CV across the histogram, indicating observed clinical changes outside of D/DDC in these models should be interpreted with caution. Centiles below 5th for all parameters show high CV and are unreliable as diffusion metrics. The stretched exponential model behaved well for both DDCα and α, making it a strong candidate for modelling multiple-b-value diffusion imaging data. KEY POINTS: • ADC has good repeatability as low 5th centile of the histogram distribution. • High CV was observed for all parameters at extremes of histogram. • Parameters from the stretched exponential model showed low coefficients of variation. • The median ADC, D, DDC α , and DDC K are highly correlated and repeatable. • Perfusion/kurtosis parameters showed high CV variations across their histogram distributions.

Feasibility and applicability of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in routine assessments of children with high-grade gliomas.

Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have been used as imaging biomarkers in adults with high-grade gliomas (HGGs). We incorporated free-breathing DW-MRI and DCE-MRI, at a single time point, in the routine follow-up of five children (median age 9 years, range 8-15) with histologically confirmed HGG within a prospective imaging study. It was feasible to incorporate DW-MRI and DCE-MRI in routine assessments of children with HGG. DW and DCE parameters were repeatable in paediatric HGG. Higher median ADC100-1000 significantly correlated with longer survival in our sample.

Diffusion-weighted MR imaging of metastatic abdominal and pelvic tumours is sensitive to early changes induced by a VEGF inhibitor using alternative diffusion attenuation models.

OBJECTIVES: To assess the utility of diffusion weighted imaging for monitoring early treatment effects associated with a VEGF inhibitor. MATERIALS AND METHODS: Twenty-nine patients with metastatic abdominal and pelvic tumours were recruited and imaged with DW-MRI: twice at baseline, and after 7 and 28 days of treatment with cediranib. Tumour measures were derived using mono-exponential, bi-exponential and stretched-exponential models, and parameter repeatability and treatment effects seen after 7 and 28 days were assessed. Correlations with volume changes and DCE-MRI metrics were also assessed. RESULTS: Diffusion coefficient repeatabilities from all models were < 6%; f and D* (bi-exponential) were 22% and 44%; α (stretched-exponential) was 4.2%. Significant increases in the diffusion coefficients from all models were observed at day 28 but not day 7. Significant decreases in D* and f.D* were observed at day 7 and in f at day 28; significant increases in α were observed at both time-points. Weak correlations between DW-MRI changes and volume changes and DCE-MRI changes were observed. CONCLUSION: DW-MRI is sensitive to early and late treatment changes caused by a VEGF inhibitor using non-mono-exponential models. Evidence of over-fitting using the bi-exponential model suggests that the stretched-exponential model is best suited to monitor such changes. KEY POINTS: • Non-mono-exponential diffusion models widen sensitivity to a broader class of tissue properties. • A stretched-exponential model robustly detects changes after 7 days of VEGF-inhibitor treatment. • There are very weak correlations between DWI-IVIM perfusion and similar DCE-MRI measures. • Diffusion-weighted MRI is a highly informative technique for assessing novel tumour therapies.

Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions.

OBJECTIVES: Pharmacokinetic (PK) modelling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data requires a reliable measure of the arterial input function (AIF) to robustly characterise tumour vascular properties. This study compared repeatability and treatment-response effects of DCE-MRI-derived PK parameters using a population-averaged AIF and three patient-specific AIFs derived from pre-bolus MRI, DCE-MRI and dynamic contrast computed tomography (DC-CT) data. METHODS: The four approaches were compared in 13 patients with abdominal metastases. Baseline repeatability [Bland-Altman statistics; coefficient of variation (CoV)], cohort percentage change and p value (paired t test) and number of patients with significant DCE-MRI parameter change post-treatment (limits of agreement) were assessed. RESULTS: Individual AIFs were obtained for all 13 patients with pre-bolus MRI and DC-CT-derived AIFs, but only 10/13 patients had AIFs measurable from DCE-MRI data. The best CoV (7.5 %) of the transfer coefficient between blood plasma and extravascular extracellular space (K (trans)) was obtained using a population-averaged AIF. All four AIF methods detected significant treatment changes: the most significant was the DC-CT-derived AIF. The population-based AIF was similar to or better than the pre-bolus and DCE-MRI-derived AIFs. CONCLUSIONS: A population-based AIF is the recommended approach for measuring cohort and individual effects since it has the best repeatability and none of the PK parameters derived using measured AIFs demonstrated an improvement in treatment sensitivity. KEY POINTS: • Pharmacokinetic modelling of DCE-MRI data requires a reliable measure of AIF. • Individual MRI-DCE-derived AIFs cannot reliably be extracted from patients. • All four AIF methods detected significant K (trans) changes after treatment. • A population-based AIF can be recommended for measuring cohort treatment responses in trials.

Modulation of renal oxygenation and perfusion in rat kidney monitored by quantitative diffusion and blood oxygen level dependent magnetic resonance imaging on a clinical 1.5T platform.

BACKGROUND: To investigate the combined use of intravoxel incoherent motion (IVIM) diffusion-weighted (DW) and blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) to assess rat renal function using a 1.5T clinical platform. METHODS: Multiple b-value DW and BOLD MR images were acquired from adult rats using a parallel clinical coil arrangement, enabling quantitation of the apparent diffusion coefficient (ADC), IVIM-derived diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f), and the transverse relaxation time T2*, for whole kidney, renal cortex, and medulla. Following the acquisition of two baseline datasets to assess measurement repeatability, images were acquired following i.v. administration of hydralazine, furosemide, or angiotensin II for up to 40 min. RESULTS: Excellent repeatability (CoV <10 %) was observed for ADC, D, f and T2* measured over the whole kidney. Hydralazine induced a marked and significant (p < 0.05) reduction in whole kidney ADC, D, and T2*, and a significant (p < 0.05) increase in D* and f. Furosemide significantly (p < 0.05) increased whole kidney ADC, D, and T2*. A more variable response to angiotensin II was determined, with a significant (p < 0.05) increase in medulla D* and significant (p < 0.05) reduction in whole kidney T2* established. CONCLUSIONS: Multiparametric MRI, incorporating quantitation of IVIM DWI and BOLD biomarkers and performed on a clinical platform, can be used to monitor the acute effects of vascular and tubular modulating drugs on rat kidney function in vivo. Clinical adoption of such functional imaging biomarkers can potentially inform on treatment effects in patients with renal dysfunction.

Modelling DW-MRI data from primary and metastatic ovarian tumours.

OBJECTIVES: To assess goodness-of-fit and repeatability of mono-exponential, stretched exponential and bi-exponential models of diffusion-weighted MRI (DW-MRI) data in primary and metastatic ovarian cancer. METHODS: Thirty-nine primary and metastatic lesions from thirty-one patients with stage III or IV ovarian cancer were examined before and after chemotherapy using DW-MRI with ten diffusion-weightings. The data were fitted with (a) a mono-exponential model to give the apparent diffusion coefficient (ADC), (b) a stretched exponential model to give the distributed diffusion coefficient (DDC) and stretching parameter (α), and (c) a bi-exponential model to give the diffusion coefficient (D), perfusion fraction (f) and pseudodiffusion coefficient (D*). RESULTS: Coefficients of variation, established from repeated baseline measurements, were: ADC 3.1%, DDC 4.3%, α 7.0%, D 13.2%, f 44.0%, D* 165.1%. The bi-exponential model was unsuitable in these data owing to poor repeatability. After excluding the bi-exponential model, analysis using Akaike Information Criteria showed that the stretched exponential model provided the better fit to the majority of pixels in 64% of lesions. CONCLUSIONS: The stretched exponential model provides the optimal fit to DW-MRI data from ovarian, omental and peritoneal lesions and lymph nodes in pre-treatment and post-treatment measurements with good repeatability. KEY POINTS: • DW-MRI data in ovarian cancer show deviation from mono-exponential behaviour • Parameters derived from the stretched exponential model showed good repeatability (CV 7%) • The bi-exponential model was unsuitable because of poor parameter repeatability • The stretched exponential model showed comparable repeatability to the mono-exponential model • The extra parameter (α) provides scope for investigation of heterogeneity or response.

Demonstration of the reproducibility of free-breathing diffusion-weighted MRI and dynamic contrast enhanced MRI in children with solid tumours: a pilot study.

OBJECTIVES: The objectives are to examine the reproducibility of functional MR imaging in children with solid tumours using quantitative parameters derived from diffusion-weighted (DW-) and dynamic contrast enhanced (DCE-) MRI. METHODS: Patients under 16-years-of age with confirmed diagnosis of solid tumours (n = 17) underwent free-breathing DW-MRI and DCE-MRI on a 1.5 T system, repeated 24 hours later. DW-MRI (6 b-values, 0-1000 sec/mm(2)) enabled monoexponential apparent diffusion coefficient estimation using all (ADC0-1000) and only ≥100 sec/mm(2) (ADC100-1000) b-values. DCE-MRI was used to derive the transfer constant (K(trans)), the efflux constant (kep), the extracellular extravascular volume (ve), and the plasma fraction (vp), using a study cohort arterial input function (AIF) and the extended Tofts model. Initial area under the gadolinium enhancement curve and pre-contrast T1 were also calculated. Percentage coefficients of variation (CV) of all parameters were calculated. RESULTS: The most reproducible cohort parameters were ADC100-1000 (CV = 3.26%), pre-contrast T1 (CV = 6.21%), and K(trans) (CV = 15.23%). The ADC100-1000 was more reproducible than ADC0-1000, especially extracranially (CV = 2.40% vs. 2.78%). The AIF (n = 9) derived from this paediatric population exhibited sharper and earlier first-pass and recirculation peaks compared with the literature's adult population average. CONCLUSIONS: Free-breathing functional imaging protocols including DW-MRI and DCE-MRI are well-tolerated in children aged 6 - 15 with good to moderate measurement reproducibility. KEY POINTS: • Diffusion MRI protocol is feasible and well-tolerated in a paediatric oncology population. • DCE-MRI for pharmacokinetic evaluation is feasible and well tolerated in a paediatric oncology population. • Paediatric arterial input function (AIF) shows systematic differences from the adult population-average AIF. • Variation of quantitative parameters from paired functional MRI measurements were within 20%.

Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling.

In addition to the diffusion coefficient, fitting the intravoxel incoherent motion model to multiple b-value diffusion-weighted MR data gives pseudo-diffusion measures associated with rapid signal attenuation at low b-values that are of use in the assessment of a number of pathologies. When summary measures are required, such as the average parameter for a region of interest, least-squares based methods give adequate estimation accuracy. However, using least-squares methods for pixel-wise fitting typically gives noisy estimates, especially for the pseudo-diffusion parameters, which limits the applicability of the approach for assessing spatial features and heterogeneity. In this article, a Bayesian approach using a shrinkage prior model is proposed and is shown to substantially reduce estimation uncertainty so that spatial features in the parameters maps are more clearly apparent. The Bayesian approach has no user-defined parameters, so measures of parameter variation (heterogeneity) over regions of interest are determined by the data alone, whereas it is shown that for the least-squares estimates, measures of variation are essentially determined by user-defined constraints on the parameters. Use of a Bayesian shrinkage prior approach is, therefore, recommended for intravoxel incoherent motion modeling.

Comparison of free-breathing with navigator-controlled acquisition regimes in abdominal diffusion-weighted magnetic resonance images: Effect on ADC and IVIM statistics.

PURPOSE: To evaluate the effect on diffusion-weighted image-derived parameters in the apparent diffusion coefficient (ADC) and intra-voxel incoherent motion (IVIM) models from choice of either free-breathing or navigator-controlled acquisition. MATERIALS AND METHODS: Imaging was performed with consent from healthy volunteers (n = 10) on a 1.5T Siemens Avanto scanner. Parameter-matched free-breathing and navigator-controlled diffusion-weighted images were acquired, without averaging in the console, for a total scan time of ∼10 minutes. Regions of interest were drawn for renal cortex, renal pyramid, whole kidney, liver, spleen, and paraspinal muscle. An ADC diffusion model for these regions was fitted for b-values ≥ 250 s/mm(2) , using a Levenberg-Marquardt algorithm, and an IVIM model was fitted for all images using a Bayesian method. RESULTS: ADC and IVIM parameters from the two acquisition regimes show no significant differences for the cohort; individual cases show occasional discrepancies, with outliers in parameter estimates arising more commonly from navigator-controlled scans. The navigator-controlled acquisitions showed, on average, a smaller range of movement for the kidneys (6.0 ± 1.4 vs. 10.0 ± 1.7 mm, P = 0.03), but also a smaller number of averages collected (3.9 ± 0.1 vs. 5.5 ± 0.2, P < 0.01) in the allocated time. CONCLUSION: Navigator triggering offers no advantage in fitted diffusion parameters, whereas free-breathing appears to offer greater confidence in fitted diffusion parameters, with fewer outliers, for matched acquisition periods.

Clinical application of machine learning models in patients with prostate cancer before prostatectomy.

BACKGROUND: To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models. METHODS: This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models' net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC). RESULTS: In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only. CONCLUSIONS: The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.

Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.

The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE. CRITICAL RELEVANT STATEMENT: The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients. PROTOCOL OF SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 . KEY POINTS: Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

¹H NMR and hyperpolarized ¹³C NMR assays of pyruvate-lactate: a comparative study.

Pyruvate-lactate exchange is mediated by the enzyme lactate dehydrogenase (LDH) and is central to the altered energy metabolism in cancer cells. The measurement of exchange kinetics using hyperpolarized (13) C NMR has provided a biomarker of response to novel therapeutics. However, the observable signal is restricted to the exchanging hyperpolarized (13) C pools and the endogenous pools of (12) C-labelled metabolites are invisible in these measurements. In this study, we investigated an alternative in vitro (1) H NMR assay, using [3-(13) C]pyruvate, and compared the measured kinetics with a hyperpolarized (13) C NMR assay, using [1-(13) C]pyruvate, under the same conditions in human colorectal carcinoma SW1222 cells. The apparent forward reaction rate constants (kPL ) derived from the two assays showed no significant difference, and both assays had similar reproducibility (kPL = 0.506 ± 0.054 and kPL = 0.441 ± 0.090 nmol/s/10(6) cells; mean ± standard deviation; n = 3); (1) H, (13) C assays, respectively). The apparent backward reaction rate constant (kLP ) could only be measured with good reproducibility using the (1) H NMR assay (kLP = 0.376 ± 0.091 nmol/s/10(6) cells; mean ± standard deviation; n = 3). The (1) H NMR assay has adequate sensitivity to measure real-time pyruvate-lactate exchange kinetics in vitro, offering a complementary and accessible assay of apparent LDH activity.

Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study.

BACKGROUND: The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability. METHODS: Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds. RESULTS: Classification performance was significant (p < 0.05, H0:AUROC = 0.5) for 11 of 20 targets using either pipeline and for these targets the AUROCs were within ± 0.05 for the two pipelines, except for one target where the Proposed pipeline performance increased by > 0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage. CONCLUSIONS: Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance. TRIAL REGISTRATION: NCT03226886 (TRACERx Renal).