Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data

Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing manual segmentations is a very time-consuming task, and highly prone to inter-observer variability, there is a need to develop robust semi-automatic segmentation models. In this work, we leverage the large and highly diverse ProstateNet dataset, which includes 638 whole gland and 461 lesion segmentation masks, from 3 different scanner manufacturers provided by 14 institutions, in addition to other 3 independent public datasets, to train accurate and robust segmentation models for the whole prostate gland, zones and lesions. We show that models trained on large amounts of diverse data are better at generalizing to data from other institutions and obtained with other manufacturers, outperforming models trained on single-institution single-manufacturer datasets in all segmentation tasks. Furthermore, we show that lesion segmentation models trained on ProstateNet can be reliably used as lesion detection models.

Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation

Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.

Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation?

OBJECTIVES

To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application.

METHODS

This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS).

RESULTS

In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44-0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44-0.73) for RFS, and 0.65 (range 0.46-0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set.

CONCLUSIONS

The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes.

CLINICAL RELEVANCE STATEMENT

Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate.

KEY POINTS

• The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44-0.68) in the randomly partitioned cohorts. • The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners. • The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.

Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness

There is a growing piece of evidence that artificial intelligence may be helpful in the entire prostate cancer disease continuum. However, building machine learning algorithms robust to inter- and intra-radiologist segmentation variability is still a challenge. With this goal in mind, several model training approaches were compared: removing unstable features according to the intraclass correlation coefficient (ICC); training independently with features extracted from each radiologist's mask; training with the feature average between both radiologists; extracting radiomic features from the intersection or union of masks; and creating a heterogeneous dataset by randomly selecting one of the radiologists' masks for each patient. The classifier trained with this last resampled dataset presented with the lowest generalization error, suggesting that training with heterogeneous data leads to the development of the most robust classifiers. On the contrary, removing features with low ICC resulted in the highest generalization error. The selected radiomics dataset, with the randomly chosen radiologists, was concatenated with deep features extracted from neural networks trained to segment the whole prostate. This new hybrid dataset was then used to train a classifier. The results revealed that, even though the hybrid classifier was less overfitted than the one trained with deep features, it still was unable to outperform the radiomics model.

A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI

Prostate cancer is one of the most common forms of cancer globally, affecting roughly one in every eight men according to the American Cancer Society. Although the survival rate for prostate cancer is significantly high given the very high incidence rate, there is an urgent need to improve and develop new clinical aid systems to help detect and treat prostate cancer in a timely manner. In this retrospective study, our contributions are twofold: First, we perform a comparative unified study of different commonly used segmentation models for prostate gland and zone (peripheral and transition) segmentation. Second, we present and evaluate an additional research question regarding the effectiveness of using an object detector as a pre-processing step to aid in the segmentation process. We perform a thorough evaluation of the deep learning models on two public datasets, where one is used for cross-validation and the other as an external test set. Overall, the results reveal that the choice of model is relatively inconsequential, as the majority produce non-significantly different scores, apart from nnU-Net which consistently outperforms others, and that the models trained on data cropped by the object detector often generalize better, despite performing worse during cross-validation.

Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.

Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of ‘non visual’ markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID‐19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x‐ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra‐low‐dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon‐counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X‐ray velocimetry integrates x‐ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.

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P-698 Pituitary functioning gonadotroph adenomas (FGA)-induced ovarian hyperstimulation syndrome (OHSS): results from tertiary neuroendocrine centres in the UK

Study question

There are no published series of OHSS due to FGA. What FGA features should clinicians look for during OHSS, and what treatments are effective?

Summary answer

FGA tumour size is always &gt;10mm. Other pituitary hormones may be deficient. Surgical resection of FGA is an effective treatment for OHSS.

What is known already

Pituitary adenomas affect 1:1000 adults and are classified as functioning or non-functioning. Non-functioning pituitary adenomas do not secrete hormones, but most commonly stain histologically gonadotroph cells. Functional pituitary adenomas secrete hormones such as prolactin causing prolactinoma. However, it is rare for a pituitary tumour to cause clinical features of excessive gonadotrophins (functioning gonadotroph adenoma; FGA).

Single case reports, but no case series, have been published on the presentation of FGA-induced OHSS in women.

Surgical excision of adenomas has been reported to cause remission of symptoms, though systematic data are lacking owing to rarity of these tumours.

Study design, size, duration

National case series from tertiary neuroendocrine units in England, Wales and Scotland.

Participants/materials, setting, methods

Eight high-volume pituitary endocrine tertiary units within England, Wales and Scotland audited their records for any cases of FGA-induced OHSS; only seven patients have been identified to date. In all cases, there had been no recent exposure to assisted reproductive technologies (ART) or drugs known to induce OHSS including gonadotrophins or selective oestrogen receptor modulators (SERMS).

Main results and the role of chance

Seven cases of FGA were identified with mean age 31.6 years (range 16-48) at diagnosis. Two-of-seven women presented acutely unwell with abdominal pain, distention and palpable mass requiring oophorectomy for ovarian torsion/ruptured ovarian cyst. The remaining five women presented with abdominal pain (n = 2), thyrotoxicosis (n = 1), menstrual irregularities/galactorrhoea (n = 1) and visual disturbances (n = 1). All women experienced intermittent pelvic pain during medical attendance. Pelvic ultrasound demonstrated enlarged multiseptated ovaries (volume ranging 27-442cm3). Ascites was noted in one woman. Six women had visual field defects due to optic chiasm compression on formal assessment. Median FSH was 26.10 u/L (8.3-33), but LH was &lt;2.5 u/L in all cases. Estradiol (E2) far exceeded the reference range in 5/7 women (2990 to &gt; 18000pmol/L);E2 was at the upper limit of normal in the remaining 2/7 women (960-1450pmol/L). Hyperprolactinaemia, hyperthyroidism and other pituitary hormones deficiency were noted in 6/7, 1/7 and 4/7 women respectively. All FGAs were macroadenomas with diameters ranging 16-48mm. Two patients were administered a somatostatin analogue prior to surgery, but FSH, E2 and tumour size did not change. Transsphenoidal surgery was performed in 6/7 women, and always improved symptomatic and biochemical features of OHSS; however, residual FGA tumour was present post-operatively in all cases studied.

Limitations, reasons for caution

It is possible that some ‘non-functioning’ gonadotroph adenomas cause subclinical problems including menstrual irregularity and mild OHSS which were never diagnosed.

We have insufficient data to determine the prognosis for future pregnancy after FGA-induced OHSS.

This study utilised historical case-notes, so some data is missing.

Wider implications of the findings

The ‘spontaneous’ presentation of OHSS may be confusing for clinicians. We report that FGA is an important cause of spontaneous OHSS which has well-defined biochemical and radiological characteristics, which may be treated effectively in the short-to-medium with pituitary surgery. Results of this study may provide greater awareness of FGA-induced OHSS.

Trial registration number

N/A

Machine learning in predicting extracapsular extension (ECE) of prostate cancer with MRI: a protocol for a systematic literature review

INTRODUCTION

In patients with prostate cancer (PCa), the detection of extracapsular extension (ECE) and seminal vesicle invasion is not only important for selecting the appropriate therapy but also for preoperative planning and patient prognosis. It is of paramount importance to stage PCa correctly before surgery, in order to achieve better surgical and outcome results. Over the last years, MRI has been incorporated in the classical prostate staging nomograms with clinical improvement accuracy in detecting ECE, but with variability between studies and radiologist's experience.

METHODS AND ANALYSIS

The research question, based on patient, index test, comparator, outcome and study design criteria, was the following: what is the diagnostic performance of artificial intelligence algorithms for predicting ECE in PCa patients, when compared with that of histopathological results after radical prostatectomy. To answer this question, we will use databases (EMBASE, PUBMED, Web of Science and CENTRAL) to search for the different studies published in the literature and we use the QUADA tool to evaluate the quality of the research selection.

ETHICS AND DISSEMINATION

This systematic review does not require ethical approval. The results will be disseminated through publication in a peer-review journal, as a chapter of a doctoral thesis and through presentations at national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42020215671.

Object detection for automatic cancer cell counting in zebrafish xenografts

Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells' size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set.

A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy

Clinically relevant postoperative pancreatic fistula (CR-POPF) is a life-threatening complication following pancreaticoduodenectomy (PD). Individualized preoperative risk assessment could improve clinical management and prevent or mitigate adverse outcomes. The aim of this study is to develop a machine learning risk model to predict occurrence of CR-POPF after PD from preoperative computed tomography (CT) scans. A total of 100 preoperative high-quality CT scans of consecutive patients who underwent pancreaticoduodenectomy in our institution between 2011 and 2019 were analyzed. Radiomic and morphological features extracted from CT scans related to pancreatic anatomy and patient characteristics were included as variables. These data were then assessed by a machine learning classifier to assess the risk of developing CR-POPF. Among the 100 patients evaluated, 20 had CR-POPF. The predictive model based on logistic regression demonstrated specificity of 0.824 (0.133) and sensitivity of 0.571 (0.337), with an AUC of 0.807 (0.155), PPV of 0.468 (0.310) and NPV of 0.890 (0.084). The performance of the model minimally decreased utilizing a random forest approach, with specificity of 0.914 (0.106), sensitivity of 0.424 (0.346), AUC of 0.749 (0.209), PPV of 0.502 (0.414) and NPV of 0.869 (0.076). Interestingly, using the same data, the model was also able to predict postoperative overall complications and a postoperative length of stay over the median with AUCs of 0.690 (0.209) and 0.709 (0.160), respectively. These findings suggest that preoperative CT scans evaluated by machine learning may provide a novel set of information to help clinicians choose a tailored therapeutic pathway in patients candidated to pancreatoduodenectomy.

Breast cancer surgery with augmented reality

Introduction: Innovations in 3D spatial technology and augmented reality imaging driven by digital high-tech industrial science have accelerated experimental advances in breast cancer imaging and the development of medical procedures aimed to reduce invasiveness. Presentation of case: A 57-year-old post-menopausal woman presented with screen-detected left-sided breast cancer. After undergoing all staging and pre-operative studies the patient was proposed for conservative breast surgery with tumor localization. During surgery, an experimental digital and non-invasive intra-operative localization method with augmented reality was compared with the standard pre-operative localization with carbon tattooing (institutional protocol). The breast surgeon wearing an augmented reality headset (Hololens) was able to visualize the tumor location projection inside the patient’s left breast in the usual supine position. Discussion: This work describes, to our knowledge, the first experimental test with a digital non-invasive method for intra-operative breast cancer localization using augmented reality to guide breast conservative surgery. In this case, a successful overlap of the previous standard pre-operative marks with carbon tattooing and tumor visualization inside the patient’s breast with augmented reality was obtained. Conclusion: Breast cancer conservative guided surgery with augmented reality can pave the way for a digital non-invasive method for intra-operative tumor localization.

DNA dosimeter measurements of beam profile using a novel simultaneous processing technique

A DNA dosimeter (DNAd) was previously developed that uses double-strand breaks (DSB) to measure dose. This dosimeter has been tested to measure dose in scenarios where transient-charged particle equilibrium (TCPE) has been established. The probability of double strand break (PDSB_o), which is the ratio of broken double-stranded DNA (dsDNA) to the initial unbroken dsDNA in the dosimeter, was used to quantify DSBs and related to dose. The goal of this work is to produce a new technique to process and analyze the DNAd and quantify DNA-DSBs. This technique included simultaneously processing multiple DNAds and also establishing a new form to the probability of double strand break (PDSB_n), which was then used to test the DNAd in a non-TCPE condition by taking beam penumbra measurements. The technique utilized a 384-well plate, and the measurements were made at the edge of a 10 × 10 cm field and compared to film measurements. During these penumbra measurements, while observing the positional differences in the higher gradient region at 4.1 and 4.55 cm from the center of the radiation field, the distance to agreement of PDSB_o to film were 0.38 cm and 0.26 cm while the distance to agreement of PDSB_n to film were 0.11 cm and 0.06 cm, respectively. Finally, the developed new separation technique reduced the time needed for the analysis of 25 samples from 200 min to 30 min.

Optimizing the response, precision, and cost of a DNA double-strand break dosimeter

We developed a dosimeter that measures biological damage following delivery of therapeutic beams in the form of double-strand breaks (DSBs) to DNA. The dosimeter contains DNA strands that are labeled on one end with biotin and on the other with fluorescein and attached to magnetic microbeads. Following irradiation, a magnet is used to separate broken from unbroken DNA strands. Then, fluorescence is utilized to measure the relative amount of broken DNA and determine the probability for DSB. The long-term goal for this research is to evaluate whether this type of biologically based dosimeter holds any advantages over the conventional techniques. The purpose of this work was to optimize the dosimeter fabrication and usage to enable higher precision for the long-term research goal. More specifically, the goal was to optimize the DNA dosimeter using three metrics: the response, precision, and cost per dosimeter. Six aspects of the dosimeter fabrication and usage were varied and evaluated for their effect on the metrics: (1) the type of magnetic microbeads, (2) the microbead to DNA mass ratio at attachment, (3) the type of suspension buffer used during irradiation, (4) the concentration of the DNA dosimeter during irradiation, (5) the time waited between fabrication and irradiation of the dosimeter, and (6) the time waited between irradiation and read out of the response. In brief, the best results were achieved with the dosimeter when attaching 4.2 µg of DNA with 1 mg of MyOne T1 microbeads and by suspending the microbead-connected DNA strands with 200 µl of phosphate-buffered saline for irradiation. Also, better results were achieved when waiting a day after fabrication before irradiating the dosimeter and also waiting an hour after irradiation to measure the response. This manuscript is meant to serve as guide for others who would like to replicate this DNA dose measurement technique.

Characterization of a novel 3D printed patient specific phantom for quality assurance in cranial stereotactic radiosurgery applications

In single-isocenter stereotactic radiosurgery/radiotherapy (SRS/SRT) intracranial applications, multiple targets are being treated concurrently, often involving non-coplanar arcs, small photon beams and steep dose gradients. In search for more rigorous quality assurance protocols, this work presents and evaluates a novel methodology for patient-specific pre-treatment plan verification, utilizing 3D printing technology. In a patient's planning CT scan, the external contour and bone structures were segmented and 3D-printed using high-density bone-mimicking material. The resulting head phantom was filled with water while a film dosimetry insert was incorporated. Patient and phantom CT image series were fused and inspected for anatomical coherence. HUs and corresponding densities were compared in several anatomical regions within the head. Furthermore, the level of patient-to-phantom dosimetric equivalence was evaluated both computationally and experimentally. A single-isocenter multi-focal SRS treatment plan was prepared, while dose distributions were calculated on both CT image series, using identical calculation parameters. Phantom- and patient-derived dose distributions were compared in terms of isolines, DVHs, dose-volume metrics and 3D gamma index (GI) analysis. The phantom was treated as if the real patient and film measurements were compared against the patient-derived calculated dose distribution. Visual inspection of the fused CT images suggests excellent geometric similarity between phantom and patient, also confirmed using similarity indices. HUs and densities agreed within one standard deviation except for the skin (modeled as 'bone') and sinuses (water-filled). GI comparison between the calculated distributions resulted in passing rates better than 97% (1%/1 mm). DVHs and dose-volume metrics were also in satisfying agreement. In addition to serving as a feasibility proof-of-concept, experimental absolute film dosimetry verified the computational study results. GI passing rates were above 90%. Results of this work suggest that employing the presented methodology, patient-equivalent phantoms (except for the skin and sinuses areas) can be produced, enabling literally patient-specific pre-treatment plan verification in intracranial applications.

Quality of life and depression in chronic sexually transmitted infections in UK and Greece: The use of WHOQOL-HIV/STI BREF

This is a comparative study aiming to investigate quality of life (QoL) and depression in individuals diagnosed either with human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS), or genital warts (GW) and genital herpes (GH), in two healthcare settings, in the United Kingdom (UK) or in Greece (Gr). Using a matched-pairs design, two equalized patient samples with sexually transmitted infections (STI) were recruited: from UK (n=43) and from Greece (n=43). QoL was assessed with WHOQOL-HIV BREF for HIV patients and WHOQOL-STI BREF -a newly adapted instrument- for genital warts and genital herpes patients. Depressive symptomatology was measured by the Centre for Epidemiological Studies- Depression Scale (CES-D) along with sociodemographic data. Results indicate that in both country- healthcare settings, a high percentage of individuals diagnosed with any type of STI, reported considerable depressive symptomatology: 35.7% for UK and 41.5% for Greek participants respectively. Regarding QoL, participants in the Greek healthcare settings reported significantly lower scores in the environment domain, and even lower scores were reported by the GW/GH group, in comparison to HIV. Specifically, these groups indicated significantly lower values in the following WHOQOL-BREF environment facets: (i) physical safety and security, (ii) participation in and opportunities for recreation/leisure activities, (iii) home environment, (iv) accessibility and quality in health and social care, and (v) transport facilities. Regarding correlation of QoL and depression, regression analysis provided significant evidence for depression having a differential effect on WHOQOL-BREF QoL domains. Evidence of increased depressive symptomatology in both STI patient- cohorts may shed light into unmet healthcare needs that should be addressed by healthcare providers in UK and Greece respectively. Furthermore, all types of Greek STI participants reported lower QoL, particularly the GW/GH group, indicating important unmet QoL needs in the environment domain, such as health and social care accessibility and quality, or environmental and social resources, all lowering everyday QoL. The present findings may provide guidelines for tailored mental health interventions alleviating depressive symptomatology in STI patients. Provision of targeted-interventions at healthcare and social-environmental levels will contribute to QoL/ health improvement in STI patients.

Perfusion Magnetic Resonance as a Biomarker for Sorafenib-Treated Advanced Hepatocellular Carcinoma: A Pilot Study

Background

Sorafenib is the currently recommended therapy in patients with advanced hepatocellular carcinoma (HCC). Among the several biomarkers available for the evaluation of the therapeutic response and prognosis, there is perfusion magnetic resonance imaging (p-MRI) that, through measurement of the vascular permeability unit (ktrans), may retrieve useful information regarding the microvascular properties of focal liver lesions. The aim of this study was to evaluate the impact of sorafenib therapy in patients with advanced HCC using the p-MRI technique.

Materials and Methods

In this retrospective study, 27 patients with the diagnosis of advanced HCC were included for palliative therapy using sorafenib. MRI of the liver was performed before the beginning of the oral therapy (T0), after 3 (T3), and after 6 months (T6). Dynamic acquisitions of the tumor (n = 50, during the first 2 min after contrast injection) were obtained in the coronal plane and were used to compute the parametric perfusion maps, acquiring the ktrans value using the extended Tofts pharmacokinetic model.

Results

The value of ktrans obtained at T0 was significantly different from the value of ktrans obtained at T6 (p = 0.028). There were no significant differences between T0 and T3 (p = 0.115) or a correlation between ktrans at T0 and the size of the lesion (p = 0.376). The ktrans value at T0 in patients with progression-free survival (PFS) > 6 months was not significantly different from the ktrans value in patients with PFS ≤6 months (p = 0.113). The ktrans value at T0 was not significantly different between patients who were previously submitted to chemoembolization and those who were not submitted (p = 0.587).

Conclusion

In this pilot study, the ktrans value may serve as a biomarker of tumor response to antiangiogenic therapy, but only 6 months after its initiation. Clinical outcomes such as PFS were not predicted before the initiation of treatment.

Evaluation and comparison of second-check monitor unit calculation software with Pinnacle3 treatment planning system

The purpose of this study was to evaluate and compare the accuracy of dose calculations in second check softwares (Diamond, IMSure, MuCheck, and RadCalc) against the Phillips Pinnacle³ treatment planning system. Eighteen previously treated patients' treatment planning files consisting of a total of 204 beams were exported from the Pinnacle³ TPS to each of the four second check software. Of these beams, 145 of the beams used were IMRT plans while 59 were VMAT arcs. The values were represented as a percent difference between primary and secondary calculations and used for statistical analysis. Box plots, Pearson Correlation, and Bland-Altman analysis were performed in MedCalc. The mean percent difference in calculated dose for Diamond, IMSure, MuCheck, and RadCalc from Pinnacle³ were -0.67%, 0.31%, 1.51% and -0.36%, respectively. The corresponding variances were calculated to be 0.07%, 0.13%, 0.08%, and 0.03%; and the largest percent differences were -7.9%, 9.70%, 9.39%, and 5.45%. The dose differences of each of the second check software in this study can vary considerably and VMAT plans have larger differences than IMRT. Among the four second check softwares, RadCalc values has shown a high agreement on average with low variation, and had the smallest percent range from Pinnacle³ values. The closest in average percent difference from the Pinnacle³data was the IMSure software, but suffered from significantly larger variance and percent range. The values reported by Diamond and MuCheck had significantly high percent differences with TPS values.

Functional and molecular MRI of the bone marrow in multiple myeloma

MRI plays an important role in the management of patients with plasma cell neoplasms and has been recognized as a biomarker of malignancy in the novel criteria for the diagnosis of multiple myeloma. Functional and molecular MRI techniques such as diffusion-weighted imaging (spinal or whole body), intravoxel incoherent motion, and dynamic contrast enhanced MRI, provide additional information related to tumor cellularity and angiogenesis, which may have prognostic implications for patients with smoldering and symptomatic myeloma. These non-invasive functional techniques are also being evaluated as imaging biomarkers for response assessment in myeloma patients. The purpose of this article is to provide a comprehensive critical review on the current use and potential future applications of these advanced MRI techniques in multiple myeloma. In addition, we will address the technologies involved and describe the qualitative and quantitative characteristics of normal bone marrow with these techniques.

Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients

Introduction

The prognostic value of the dynamic contrast-enhanced (DCE) MRI perfusion and its histogram analysis-derived metrics is not well established for high-grade glioma (HGG) patients. The aim of this prospective study was to investigate DCE perfusion transfer coefficient (K^trans), vascular plasma volume fraction (v_p), extracellular volume fraction (v_e), reverse transfer constant (k_ep), and initial area under gadolinium concentration time curve (IAUGC) as predictors of progression-free (PFS) and overall survival (OS) in HGG patients.

Methods

Sixty-nine patients with suspected anaplastic astrocytoma or glioblastoma underwent preoperative DCE-MRI scans. DCE perfusion whole tumor region histogram parameters, clinical details, and PFS and OS data were obtained. Univariate, multivariate, and Kaplan–Meier survival analyses were conducted. Receiver operating characteristic (ROC) curve analysis was employed to identify perfusion parameters with the best differentiation performance.

Results

On univariate analysis, v_e and skewness of v_p had significant negative impacts, while k_ep had significant positive impact on OS (P < 0.05). v_e was also a negative predictor of PFS (P < 0.05). Patients with lower v_e and IAUGC had longer median PFS and OS on Kaplan–Meier analysis (P < 0.05). K^trans and v_e could also differentiate grade III from IV gliomas (area under the curve 0.819 and 0.791, respectively).

Conclusions

High v_e is a consistent predictor of worse PFS and OS in HGG glioma patients. v_p skewness and k_ep are also predictive for OS. K^trans and v_e demonstrated the best diagnostic performance for differentiating grade III from IV gliomas.

Pediatric Cranio-spinal Axis Irradiation

Pediatric cranio-spinal axis irradiation (CSI) is a valuable treatment for many central nervous system (CNS) diseases, but due to the life expectancies and quality of life expectations for children, the minimization of the risk for radiation-induced secondary malignancies must be a high priority. This study compared the estimated CSI-induced secondary malignancy risks of three radiation therapy modalities using three different models. Twenty-four ( n = 24) pediatric patients previously treated with CSI for tumors of the CNS were planned using three different treatment modalities: three-dimensional conformal radiation therapy (3D-CRT), volume modulated arc therapy (VMAT), and Tomotherapy. Each plan was designed to deliver 23.4 Gy (1.8 Gy/fraction) to the target which was defined as the entire brain and spinal column with a 0.7 cm expansion. The mean doses as well as the dose volume histograms (DVH) of specific organs were analyzed for secondary malignancy risk according to three different methods: the effective dose equivalent (EDE), the excess relative risk (ERR), and the linear quadratic (LQ) models. Using the EDE model, the average secondary risk was highest for the 3D-CRT plans (37.60%), compared to VMAT (28.05%) and Tomotherapy (27.90%). The ERR model showed similarly that the 3D-CRT plans had considerably higher risk (10.84%) than VMAT and Tomotherapy, which showed almost equal risks (7.05 and 7.07%, respectively). The LQ model requires organ-specific cell survival parameters, which for the lungs, heart, and breast relevant values were found and applied. The lung risk for secondary malignancy was found to be 1.00, 1.96, and 2.07% for 3D-CRT, VMAT, and Tomotherapy, respectively. The secondary cancer risk for breast was estimated to be 0.09, 0.21, and 0.27% and for heart it was 9.75, 6.02 and 6.29% for 3D-CRT, VMAT, and Tomotherapy, respectively. Based on three methods of secondary malignancy estimation, the 3D-CRT plans produced highest radiation-induced secondary malignancy risk, and the VMAT and Tomotherapy plans had nearly equal risk. Pediatric patients must be treated with reducing long term sequelae as a priority.