METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

PURPOSE

To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies.

METHODS

We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated.

RESULT

In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community.

CONCLUSION

In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers.

CRITICAL RELEVANCE STATEMENT

A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning.

KEY POINTS

• A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Cancer Marker Immunosensing through Surface-Enhanced Photoluminescence on Nanostructured Silver Substrates

Nanostructured noble metal surfaces enhance the photoluminescence emitted by fluorescent molecules, permitting the development of highly sensitive fluorescence immunoassays. To this end, surfaces with silicon nanowires decorated with silver nanoparticles in the form of dendrites or aggregates were evaluated as substrates for the immunochemical detection of two ovarian cancer indicators, carbohydrate antigen 125 (CA125) and human epididymis protein 4 (HE4). The substrates were prepared by metal-enhanced chemical etching of silicon wafers to create, in one step, silicon nanowires and silver nanoparticles on top of them. For both analytes, non-competitive immunoassays were developed using pairs of highly specific monoclonal antibodies, one for analyte capture on the substrate and the other for detection. In order to facilitate the identification of the immunocomplexes through a reaction with streptavidin labeled with Rhodamine Red-X, the detection antibodies were biotinylated. An in-house-developed optical set-up was used for photoluminescence signal measurements after assay completion. The detection limits achieved were 2.5 U/mL and 3.12 pM for CA125 and HE4, respectively, with linear dynamic ranges extending up to 500 U/mL for CA125 and up to 500 pM for HE4, covering the concentration ranges of both healthy and ovarian cancer patients. Thus, the proposed method could be implemented for the early diagnosis and/or prognosis and monitoring of ovarian cancer.

SERS Immunosensors for Cancer Markers Detection

Early diagnosis and monitoring are essential for the effective treatment and survival of patients with different types of malignancy. To this end, the accurate and sensitive determination of substances in human biological fluids related to cancer diagnosis and/or prognosis, i.e., cancer biomarkers, is of ultimate importance. Advancements in the field of immunodetection and nanomaterials have enabled the application of new transduction approaches for the sensitive detection of single or multiple cancer biomarkers in biological fluids. Immunosensors based on surface-enhanced Raman spectroscopy (SERS) are examples where the special properties of nanostructured materials and immunoreagents are combined to develop analytical tools that hold promise for point-of-care applications. In this frame, the subject of this review article is to present the advancements made so far regarding the immunochemical determination of cancer biomarkers by SERS. Thus, after a short introduction about the principles of both immunoassays and SERS, an extended presentation of up-to-date works regarding both single and multi-analyte determination of cancer biomarkers is presented. Finally, future perspectives on the field of SERS immunosensors for cancer markers detection are briefly discussed.

SERS Determination of Oxidative Stress Markers in Saliva Using Substrates with Silver Nanoparticle-Decorated Silicon Nanowires

Glutathione and malondialdehyde are two compounds commonly used to evaluate the oxidative stress status of an organism. Although their determination is usually performed in blood serum, saliva is gaining ground as the biological fluid of choice for oxidative stress determination at the point of need. For this purpose, surface-enhanced Raman spectroscopy (SERS), which is a highly sensitive method for the detection of biomolecules, could offer additional advantages regarding the analysis of biological fluids at the point of need. In this work, silicon nanowires decorated with silver nanoparticles made by metal-assisted chemical etching were evaluated as substrates for the SERS determination of glutathione and malondialdehyde in water and saliva. In particular, glutathione was determined by monitoring the reduction in the Raman signal obtained from substrates modified with crystal violet upon incubation with aqueous glutathione solutions. On the other hand, malondialdehyde was detected after a reaction with thiobarbituric acid to produce a derivative with a strong Raman signal. The detection limits achieved after optimization of several assay parameters were 50 and 3.2 nM for aqueous solutions of glutathione and malondialdehyde, respectively. In artificial saliva, however, the detection limits were 2.0 and 0.32 μM for glutathione and malondialdehyde, respectively, which are, nonetheless, adequate for the determination of these two markers in saliva.

Enhanced Photoluminescence of R6G Dyes from Metal Decorated Silicon Nanowires Fabricated through Metal Assisted Chemical Etching

In this study, we developed active substrates consisting of Ag-decorated silicon nanowires on a Si substrate using a single-step Metal Assisted Chemical Etching (MACE) process, and evaluated their performance in the identification of low concentrations of Rhodamine 6G using surface-enhanced photoluminescence spectroscopy. Different structures with Ag-aggregates as well as Ag-dendrites were fabricated and studied depending on the etching parameters. Moreover, the addition of Au nanoparticles by simple drop-casting on the MACE-treated surfaces can enhance the photoluminescence significantly, and the structures have shown a Limit of Detection of Rhodamine 6G down to 10-12 M for the case of the Ag-dendrites enriched with Au nanoparticles.

APOBEC3B Is Co-Expressed with PKCα/NF-κB in Oral and Oropharyngeal Squamous Cell Carcinomas

The enzymatic activity of APOBEC3B (A3B) has been implicated as a prime source of mutagenesis in head and neck squamous cell carcinoma (HNSCC). The expression of Protein Kinase C α (PKCα) and Nuclear Factor-κΒ p65 (NF-κΒ p65) has been linked to the activation of the classical and the non-canonical NF-κB signaling pathways, respectively, both of which have been shown to lead to the upregulation of A3B. Accordingly, the aim of the present study was to evaluate the expression of PKCα, NF-κΒ p65 and A3B in non-HPV related oral and oropharyngeal squamous cell carcinomas (SCC), by means of immunohistochemistry and in silico methods. PKCα was expressed in 29/36 (80%) cases of oral and oropharyngeal SCCs, with 25 (69%) cases showing a PKCα+/A3B+ phenotype and only 6/36 (17%) cases showing a PKCα-/A3B+ phenotype. Εxpression of NF-κB p65 was seen in 33/35 (94%) cases of oral and oropharyngeal SCCs, with 30/35 (86%) cases showing an NF-κB p65+/A3B+ phenotype and only 2/35 (6%) cases showing an NF-κB p65-/A3B+ phenotype. In addition, mRNA expression analysis, using the UALCAN database, revealed strong expression of all three genes. These findings indicate that the expression of A3B is associated with PKCα/NF-κB p65 expression and suggest a potential role for the PKC/NF-κB signaling pathway in the development of oral and oropharyngeal cancer.

Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review

Contemporary deep learning-based decision systems are well-known for requiring high-volume datasets in order to produce generalized, reliable, and high-performing models. However, the collection of such datasets is challenging, requiring time-consuming processes involving also expert clinicians with limited time. In addition, data collection often raises ethical and legal issues and depends on costly and invasive procedures. Deep generative models such as generative adversarial networks and variational autoencoders can capture the underlying distribution of the examined data, allowing them to create new and unique instances of samples. This study aims to shed light on generative data augmentation techniques and corresponding best practices. Through in-depth investigation, we underline the limitations and potential methodology pitfalls from critical standpoint and aim to promote open science research by identifying publicly available open-source repositories and datasets.

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 >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 <2.5 u/L in all cases. Estradiol (E2) far exceeded the reference range in 5/7 women (2990 to > 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

Comparison of MRI Features of Fat Fraction and ADC for Early Treatment Response Assessment in Participants with Multiple Myeloma

Background An imaging-based predictor of response could provide prognostic information early during treatment course in patients with multiple myeloma (MM). Purpose To investigate if very early changes in bone marrow relative fat fraction (rFF) and apparent diffusion coefficient (ADC) histogram metrics, occurring after one cycle of induction therapy in participants with newly diagnosed MM, could help predict overall best response status. Materials and Methods This prospective study included participants with MM who were enrolled between August 2014 and December 2017. Histogram metrics were extracted from ADC and rFF maps from MRI examinations performed before treatment and after the first treatment cycle. Participants were categorized into the very good partial response (VGPR) or better group and the less than VGPR group per the International Myeloma Working Group response criteria. ADC and rFF map metrics for predicting treatment response were compared using the Wilcoxon rank test, and the false discovery rate (FDR) was used to correct for multiple comparisons. Results A total of 23 participants (mean age, 65 years ± 11 [SD]; 13 men) were evaluated. There was no evidence of a difference in ADC metrics between the two responder groups after correcting for multiple comparisons. The rFF histogram changes between pretreatment MRI and MRI after the first treatment cycle (ΔrFF) that provided significant differences between the VGPR or better and less than VGPR groups were as follows: ΔrFF_10th Percentile (median, 0.5 [95% CI: 0, 1] vs -2.5 [95% CI: -5.1, 0.1], respectively), ΔrFF_90th Percentile (median, 2 [95% CI: 1, 6.8] vs -0.5 [95% CI: -1, 0]), ΔrFF_Mean (median, 3.4 [95% CI: 0.3, 7.6] vs -1.1 [95% CI: -1.8, -0.7]), and ΔrFF_Root Mean Squared (median, 3.2 [95% CI: 0.3, 6.1] vs -0.7 [95% CI: -1.3, -0.4]) (FDR-adjusted P = .03 for all), and the latter two also presented mean group increases in the VGPR or better group that were above the upper 95% CI limit for repeatability. Conclusion Very early changes in bone marrow relative fat fraction histogram metrics, calculated from MRI examination at baseline and after only one cycle of induction therapy, may help to predict very good partial response or better in participants with newly diagnosed multiple myeloma. © RSNA, 2022 Online supplemental material is available for this article.

Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results

Simple Summary

Radiogenomics is a promising new approach in cancer assessment, providing an evaluation of the molecular basis of imaging phenotypes after establishing associations between radiological features and molecular features at the genomic–transcriptomic–proteomic level. This review focuses on describing key aspects of radiogenomics while discussing limitations of translatability to the clinic and possible solutions to these challenges, providing the clinician with an up-to-date handbook on how to use this new tool.

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, and elucidation of its complicated pathobiology has been traditionally targeted by studies incorporating genomic as well other high-throughput approaches. Recently, a collection of methods used for cancer imaging, supplemented by quantitative aspects leading towards imaging biomarker assessment termed “radiomics”, has introduced a novel dimension in cancer research. Integration of genomics and radiomics approaches, where identifying the biological basis of imaging phenotypes is feasible due to the establishment of associations between molecular features at the genomic–transcriptomic–proteomic level and radiological features, has recently emerged termed radiogenomics. This review article aims to briefly describe the main aspects of radiogenomics, while discussing its basic limitations related to lung cancer clinical applications for clinicians, researchers and patients.

Branch-duct intraductal papillary mucinous neoplasm (IPMN): Are cyst volumetry and other novel imaging features able to improve malignancy prediction compared to well-established resection criteria?

OBJECTIVES

Current guidelines base the management of intraductal papillary mucinous neoplasms (IPMN) on several well-established resection criteria (RC), including cyst size. However, malignancy may occur in small cysts. Since branch-duct (BD) IPMN are not perfect spheres, volumetric and morphologic analysis might better correlate with mucin production and grade of dysplasia. Nonetheless, their role in malignancy (high-grade dysplasia/invasive cancer) prediction has been poorly investigated. Previous studies evaluating RC also included patients with solid-mass-forming pancreatic cancer (PC), which may affect the RC yield. This study aimed to assess the role of volume, morphology, and other well-established RC in malignancy prediction in patients with BD- and mixed-type IPMN after excluding solid masses.

METHODS

Retrospective ethical review-board-approved study of 106 patients (2008-2019) with histopathological diagnosis of BD- and mixed-type IPMN (without solid masses) and preoperative MRI available. Standard imaging and clinical features were collected, and the novel imaging features cyst-volume and elongation value [EV = 1 - (width/length)] calculated on T2-weighted images. Logistic regression analysis was performed. Statistical significance set at two-tails, p < 0.05.

RESULTS

Neither volume (odds ratio (OR) = 1.01, 95% CI: 0.99-1.02, p = 0.12) nor EV (OR = 0.38, 95% CI: 0.02-5.93, p = 0.49) was associated with malignancy. Contrast-enhancing mural nodules (MN), main pancreatic duct (MPD) ≥ 5 mm, and elevated carbohydrate antigen (CA) 19-9 serum levels (> 37 μmol/L) were associated with malignancy (MN OR: 4.32, 95% CI: 1.18-15.76, p = 0.02; MPD ≥ 5 mm OR: 4.2, 95% CI: 1.34-13.1, p = 0.01; CA19-9 OR: 6.72; 95% CI: 1.89 - 23.89, p = 0.003).

CONCLUSIONS

Volume and elongation value cannot predict malignancy in BD- and/or mixed-type IPMN. Mural nodules, MPD ≥ 5 mm and elevated CA19-9 serum levels are associated with higher malignancy risk even after the exclusion of solid masses.

KEY POINTS

• Novel and well-established resection criteria for IPMN have been evaluated after excluding solid masses. • BD-IPMN volume and elongation value cannot predict malignancy. • Main pancreatic duct ≥ 5 mm, mural nodules, and elevated carbohydrate antigen 19-9 levels are associated with malignancy.

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.

Improving performance and generalizability in radiogenomics: a pilot study for prediction of IDH1/2 mutation status in gliomas with multicentric data

Purpose: Radiogenomics offers a potential virtual and noninvasive biopsy. However, radiogenomics models often suffer from generalizability issues, which cause a performance degradation on unseen data. In MRI, differences in the sequence parameters, manufacturers, and scanners make this generalizability issue worse. Such image acquisition information may be used to define different environments and select robust and invariant radiomic features associated with the clinical outcome that should be included in radiomics/radiogenomics models. Approach: We assessed 77 low-grade gliomas and glioblastomas multiform patients publicly available in TCGA and TCIA. Radiomics features were extracted from multiparametric MRI images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery) and different regions-of-interest (enhancing tumor, nonenhancing tumor/necrosis, and edema). A method developed to find variables that are part of causal structures was used for feature selection and compared with an embedded feature selection approach commonly used in radiomics/radiogenomics studies, across two different scenarios: (1) leaving data from a center as an independent held-out test set and tuning the model with the data from the remaining centers and (2) use stratified partitioning to obtain the training and the held-out test sets. Results: In scenario (1), the performance of the proposed methodology and the traditional embedded method was AUC: 0.75 [0.25; 1.00] versus 0.83 [0.50; 1.00], Sens.: 0.67 [0.20; 0.93] versus 0.67 [0.20; 0.93], Spec.: 0.75 [0.30; 0.95] versus 0.75 [0.30; 0.95], and MCC: 0.42 [0.19; 0.68] versus 0.42 [0.19; 0.68] for center 1 as the held-out test set. The performance of both methods for center 2 as the held-out test set was AUC: 0.64 [0.36; 0.91] versus 0.55 [0.27; 0.82], Sens.: 0.00 [0.00; 0.73] versus 0.00 [0.00; 0.73], Spec.: 0.82 [0.52; 0.94] versus 0.91 [0.62; 0.98], and MCC: - 0.13 [ - 0.38 ; - 0.04 ] versus - 0.09 [ - 0.38 ; - 0.02 ] , whereas for center 3 was AUC: 0.80 [0.62; 0.95] versus 0.89 [0.56; 0.96], Sens.: 0.86 [0.48; 0.97] versus 0.86 [0.48; 0.97], Spec.: 0.72 [0.54; 0.85] versus 0.79 [0.61; 0.90], and MCC: 0.47 [0.41; 0.53] versus 0.55 [0.48; 0.60]. For center 4, the performance of both methods was AUC: 0.77 [0.51; 1.00] versus 0.75 [0.47; 0.97], Sens.: 0.53 [0.30; 0.75] versus 0.00 [0.00; 0.15], Spec.: 0.71 [0.35; 0.91] versus 0.86 [0.48; 0.97], and MCC: 0.23 [0.16; 0.31] versus. - 0.32 [ - 0.46 ; - 0.20 ] . In scenario (2), the performance of these methods was AUC: 0.89 [0.71; 1.00] versus 0.79 [0.58; 0.94], Sens.: 0.86 [0.80; 0.92] versus 0.43 [0.15; 0.74], Spec.: 0.87 [0.62; 0.96] versus 0.87 [0.62; 0.96], and MCC: 0.70 [0.60; 0.77] versus 0.33 [0.24; 0.42]. Conclusions: This proof-of-concept study demonstrated good performance by the proposed feature selection method in the majority of the studied scenarios, as it promotes robustness of features included in the models and the models' generalizability by making used imaging data of different scanners or with sequence parameters.

Diffusion tensor-based fiber tracking of the male urethral sphincter complex in patients undergoing radical prostatectomy: a feasibility study

Objectives

To study the diffusion tensor-based fiber tracking feasibility to access the male urethral sphincter complex of patients with prostate cancer undergoing Retzius-sparing robot-assisted laparoscopic radical prostatectomy (RS-RARP).

Methods

Twenty-eight patients (median age of 64.5 years old) underwent 3 T multiparametric-MRI of the prostate, including an additional echo-planar diffusion tensor imaging (DTI) sequence, using 15 diffusion-encoding directions and a b value = 600 s/mm². Acquisition parameters, together with patient motion and eddy currents corrections, were evaluated. The proximal and distal sphincters, and membranous urethra were reconstructed using the deterministic fiber assignment by continuous tracking (FACT) algorithm, optimizing fiber tracking parameters. Tract length and density, fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) were computed. Regional differences between structures were accessed by ANOVA, or nonparametric Kruskal–Wallis test, and post-hoc tests were employed, respectively, TukeyHSD or Dunn’s.

Results

The structures of the male urethral sphincter complex were clearly depicted by fiber tractography using optimized acquisition and fiber tracking parameters. The use of eddy currents and subject motion corrections did not yield statistically significant differences on the reported DTI metrics. Regional differences were found between all structures studied among patients, suggesting a quantitative differentiation on the structures based on DTI metrics.

Conclusions

The current study demonstrates the technical feasibility of the proposed methodology, to study in a preoperative setting the male urethral sphincter complex of prostate cancer patients candidates for surgical treatment. These findings may play a role on a more accurate prediction of the RS-RARP post-surgical urinary continence recovery rate.

Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.

CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas

To assess the performance of CT-based radiomics analysis in differentiating benign from malignant intraductal papillary mucinous neoplasms of the pancreas (IPMN), preoperative scans of 408 resected patients with IPMN were retrospectively analyzed. IPMNs were classified as benign (low-grade dysplasia, n = 181), or malignant (high grade, n = 128, and invasive, n = 99). Clinicobiological data were reported. Patients were divided into a training cohort (TC) of 296 patients and an external validation cohort (EVC) of 112 patients. After semi-automatic tumor segmentation, PyRadiomics was used to extract radiomics features. A multivariate model was developed using a logistic regression approach. In the training cohort, 85/107 radiomics features were significantly different between patients with benign and malignant IPMNs. Unsupervised clustering analysis revealed four distinct clusters of patients with similar radiomics features patterns with malignancy as the most significant association. The multivariate model differentiated benign from malignant tumors in TC with an area under the ROC curve (AUC) of 0.84, sensitivity (Se) of 0.82, specificity (Spe) of 0.74, and in EVC with an AUC of 0.71, Se of 0.69, Spe of 0.57. This large study confirms the high diagnostic performance of preoperative CT-based radiomics analysis to differentiate between benign from malignant IPMNs.

A multicenter study on radiomic features from T2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics

PURPOSE

To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features.

METHODS

T₂ -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images.

RESULTS

From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information.

CONCLUSION

We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.

New boundaries of liver imaging: from morphology to function

From an invisible organ to one of the most explored non-invasively, the liver is, today, one of the cornerstones for current cross-sectional imaging techniques and minimally invasive procedures. After the achievements of US, CT and, most recently, MRI in providing highly accurate morphological and structural information about the organ, a significant scientific development has gained momentum for the last decades, coupling morphology to liver function and contributing far most to what we know today as precision medicine. In fact, dedicated tailor-made investigations are now possible in order to detect and, most of all, quantify physiopathological processes with unprecedented certitude. It is the intention of this review to provide a better insight to the reader of several functional imaging techniques applied to liver imaging. Contrast enhanced imaging, diffusion weighted imaging, elastography, spectral computed tomography and fat and iron assessment techniques are commonly performed clinically. Diffusion kurtosis imaging, magnetic resonance spectroscopy, T1 relaxometry and radiomics remain largely limited to advanced clinical research. Each of them has its own value and place on the diagnostic armamentarium and provide unique qualitative and quantitative information regarding the pathophysiology of diseases, contributing at a large scale to model therapeutic decisions and patient follow-up. Therefore, state-of-the-art liver imaging acts today as a non-invasive surrogate biomarker of many focal and diffuse liver diseases.

Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays

COVID-19 has led to an unprecedented healthcare crisis with millions of infected people across the globe often pushing infrastructures, healthcare workers and entire economies beyond their limits. The scarcity of testing kits, even in developed countries, has led to extensive research efforts towards alternative solutions with high sensitivity. Chest radiological imaging paired with artificial intelligence (AI) can offer significant advantages in diagnosis of novel coronavirus infected patients. To this end, transfer learning techniques are used for overcoming the limitations emanating from the lack of relevant big datasets, enabling specialized models to converge on limited data, as in the case of X-rays of COVID-19 patients. In this study, we present an interpretable AI framework assessed by expert radiologists on the basis on how well the attention maps focus on the diagnostically-relevant image regions. The proposed transfer learning methodology achieves an overall area under the curve of 1 for a binary classification problem across a 5-fold training/testing dataset.

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.

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

The new era of artificial intelligence (AI) has introduced revolutionary data-driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision-support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed.

How to develop a meaningful radiomic signature for clinical use in oncologic patients

During the last decade, there is an increasing usage of quantitative methods in Radiology in an effort to reduce the diagnostic variability associated with a subjective manner of radiological interpretation. Combined approaches where visual assessment made by the radiologist is augmented by quantitative imaging biomarkers are gaining attention. Advances in machine learning resulted in the rise of radiomics that is a new methodology referring to the extraction of quantitative information from medical images. Radiomics are based on the development of computational models, referred to as “Radiomic Signatures”, trying to address either unmet clinical needs, mostly in the field of oncologic imaging, or to compare radiomics performance with that of radiologists. However, to explore this new technology, initial publications did not consider best practices in the field of machine learning resulting in publications with questionable clinical value. In this paper, our effort was concentrated on how to avoid methodological mistakes and consider critical issues in the workflow of the development of clinically meaningful radiomic signatures.

UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI

Background

Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists.

Methods

An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area’s signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed.

Results

The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient.

Conclusions

The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment.

Lung Cryobiopsy for the Diagnosis of Interstitial Lung Diseases: A Series Contribution to a Debated Procedure

Introduction: Transbronchial cryobiopsy is an alternative to surgical biopsy for the diagnosis of fibrosing interstitial lung diseases, although the role of this relatively new method is rather controversial. Aim of this study is to evaluate the diagnostic performance and the safety of transbronchial cryobiopsy in patients with fibrosing interstitial lung disease. Materials and methods: The population in this study included patients with interstitial lung diseases who underwent cryobiopsy from May 2015 to May 2018 at the Division of Pneumology of San Giuseppe Hospital in Milan and who were retrospectively studied. All cryobiopsy procedures were performed under fluoroscopic guidance using a flexible video bronchoscope and an endobronchial blocking system in the operating room with patients under general anaesthesia. The diagnostic performance and safety of the procedure were assessed. The main complications evaluated were endobronchial bleeding and pneumothorax. All cases were studied with a multidisciplinary approach, before and after cryobiopsy. Results: Seventy-three patients were admitted to this study. A specific diagnosis was reached in 64 cases, with a diagnostic sensitivity of 88%; 5 cases (7%) were considered inadequate, 4 cases (5%) were found to be non-diagnostic. Only one major bleeding event occurred (1.4%), while 14 patients (19%) experienced mild/moderate bleeding events while undergoing bronchoscopy; 8 cases of pneumothorax (10.9%) were reported, of which 2 (2.7%) required surgical drainage. Conclusions: When performed under safe conditions and in an experienced center, cryobiopsy is a procedure with limited complications having a high diagnostic yield in fibrotic interstitial lung disease.

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.

The Gene Toolkit Implicated in Functional Sex in Sparidae Hermaphrodites: Inferences From Comparative Transcriptomics

Sex-biased gene expression is the mode through which sex dimorphism arises from a nearly identical genome, especially in organisms without genetic sex determination. Teleost fishes show great variations in the way the sex phenotype forms. Among them, Sparidae, that might be considered as a model family displays a remarkable diversity of reproductive modes. In this study, we sequenced and analyzed the sex-biased transcriptome in gonads and brain (the tissues with the most profound role in sexual development and reproduction) of two sparids with different reproductive modes: the gonochoristic common dentex, Dentex dentex, and the protandrous hermaphrodite gilthead seabream, Sparus aurata. Through comparative analysis with other protogynous and rudimentary protandrous sparid transcriptomes already available, we put forward common male and female-specific genes and pathways that are probably implicated in sex-maintenance in this fish family. Our results contribute to the understanding of the complex processes behind the establishment of the functional sex, especially in hermaphrodite species and set the groundwork for future experiments by providing a gene toolkit that can improve efforts to control phenotypic sex in finfish in the ever-increasingly important field of aquaculture.

Pathologic Grading of Malignant Pleural Mesothelioma: An Evidence-Based Proposal

INTRODUCTION

A pathologic grading system (PGS) for malignant pleural mesothelioma (MPM) is warranted to better identify different risk categories of patients, plan therapeutic options, and activate clinical trials.

METHODS

A series of 940 patients with MPM (328 in a training set and 612 in a validation set) that was diagnosed between October 1980 and June 2015 at the participant institutions was retrospectively assembled. A PGS was constructed by attributing to each histologic parameter, independent at multivariate analysis with excellent reproducibility (κ > 0.75), different scores based on the increase in corresponding hazard ratios. The relevant PGS score thus ranged from 0 to 8 points for individual patients with MPM.

CONCLUSIONS

The PGS was constructed by taking into consideration the histological subtyping of MPM (epithelioid/biphasic = 0 points; sarcomatoid = 2 points), necrosis (absent = 0 points versus present = 1 point), mitotic count per 1 mm² (cutoffs as follows: 1-2 = 0 points, 3-5 = 1 point, 6-9 = 2 points, or ≥10 = 4 points), and Ki-67 labeling index based on 2000 cells (<30% = 0 points versus ≥30 = 1 point), all of which are independent factors in both patient sets after adjustment for stage and age at diagnosis. No heterogeneity was seen across the validation centers (p = 0.19). Epithelioid/biphasic MPM patterning and biopsy versus resection did not affect survival, whereas the PGS outperformed mitotic count and Ki-67 LI in both the training (area under the curve receiver operating characteristic = 0.76) and validation sets (area under the curve receiver operating characteristic = 0.73) (p < 0.01). Patient survival progressively deteriorated from a score of 0 (median times of 26.3 and 26.9 months) to a score 1 to 3 (median times of 12.8 and 14.4 months) and a score of 4 to 8 (median times of 3.7 and 7.7 months) in both sets of patients, with the hazard ratio for a 1-point increase in score being 1.46 (95% confidence interval: 1.36-1.56) in the training set and 1.28 (95% confidence interval: 1.22-1.34) in the validation set (after adjustment for age and [when available] tumor stage). The PGS was effective even in subgroup analysis (epithelioid, biphasic, and sarcomatoid tumors).

DISCUSSION

A simple and reproducible multiparametric PGS effectively predicted survival in patients with MPM.

Correction: Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

[This corrects the article DOI: 10.1371/journal.pone.0184197.].

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.

Classification of pulmonary neuroendocrine tumors: new insights

Neuroendocrine tumors of the lung (Lu-NETs) embrace a heterogeneous family of neoplasms classified into four histological variants, namely typical carcinoid (TC), atypical carcinoid (AC), large cell neuroendocrine carcinoma (LCNEC) and small cell lung carcinoma (SCLC). Defining criteria on resection specimens include mitotic count in 2 mm2 and the presence or absence of necrosis, alongside a constellation of cytological and histological traits including cell size and shape, nuclear features and overall architecture. Clinically, TC are low-grade malignant tumors, AC intermediate-grade malignant tumors and SCLC/LCNEC high-grade malignant full-blown carcinomas with no significant differences in survival between them. Homologous tumors arise in the thymus that occasionally have some difficulties in differentiating from the lung counterparts when presented with large unresectable or metastatic lesions. Immunohistochemistry (IHC) helps refine NE diagnosis at various anatomical sites, particularly on small-sized tissue material, in which only TC and small cell carcinoma categories can be recognized easily on hematoxylin & eosin stain, while AC and LCNEC can only be suggested on such material. The Ki-67 labeling index effectively separates carcinoids from small cell carcinoma and may prove useful for the clinical management of a metastatic disease to help the therapeutic decision-making process. Although carcinoids and high-grade neuroendocrine carcinomas in the lung and elsewhere make up separate tumor categories on molecular grounds, emerging data supports the concept of secondary high-grade NETs arising in the preexisting carcinoids, whose clinical and biological relevance will have to be placed into the proper context for the optimal management of these patients. In this review, we will discuss the selected, recent literature with a focus on current issues regarding Lu-NET nosology, i.e., classification, derivation and tumor evolution.

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

Purpose

The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer.

Material and methods

Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm²) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R² and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio.

Results

All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.

Conclusion

No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

Correlation between quantitative and semiquantitative parameters in DCE-MRI with a blood pool agent in rectal cancer: can semiquantitative parameters be used as a surrogate for quantitative parameters?

PURPOSE

The aim of this study was to assess correlation between quantitative and semiquantitative parameters in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in rectal cancer patients, both in a primary staging and restaging setting.

MATERIALS AND METHODS

Nineteen patients were included with DCE-MRI before and/or after neoadjuvant therapy. DCE-MRI was performed with gadofosveset trisodium (Ablavar^®, Lantheus Medical Imaging, North Billerica, Massachusetts, USA). Regions of interest were placed in the tumor and quantitative parameters were extracted with Olea Sphere 2.2 software permeability module using the extended Tofts model. Semiquantitative parameters were calculated on a pixel-by-pixel basis. Spearman rank correlation tests were used for assessment of correlation between parameters. A p value ≤0.05 was considered statistically significant.

RESULTS

Strong positive correlations were found between mean peak enhancement and mean K _trans: 0.79 (all patients, p<0.0001), 0.83 (primary staging, p = 0.003), and 0.81 (restaging, p = 0.054). Mean wash-in correlated significantly with mean V _p and K _ep (0.79 and 0.58, respectively, p<0.0001 and p = 0.009) in all patients. Mean wash-in showed a significant correlation with mean K _ep (0.67, p = 0.033) in the primary staging group. On the restaging MRI, mean wash-in only strongly correlated with mean V _p (0.81, p = 0.054).

CONCLUSION

This study shows a strong correlation between quantitative and semiquantitative parameters in DCE-MRI for rectal cancer. Peak enhancement correlates strongly with K _trans and wash-in showed strong correlation with V _p and K _ep. These parameters have been reported to predict tumor aggressiveness and response in rectal cancer. Therefore, semiquantitative analyses might be a surrogate for quantitative analyses.