Tumor Burden of Iodine-Avid Bone Metastatic Thyroid Cancer Identified via 18F-Sodium Fluoride PET/CT Imaging

BACKGROUND

Patients with differentiated thyroid cancer (DTC) are referred to radioactive 131I (RAI) therapy and post-therapy 131I whole-body scintigraphy (WBS) to identify local and/or remote metastases. Positron emission tomography (PET)/computed tomography (CT) imaging with 18F-fluoro-D-glucose (FDG) or 18F-sodium fluoride (NaF) may also be used with these patients for the evaluation of bone metastases. We compared the role of 18F-NaF PET/CT and 18F-FDG-PET/CT in patients with DTC and documented bone metastases at post-therapy WBS.

METHODS

Ten consecutive DTC patients with iodine avid bone metastasis at post-therapy WBS referred to 18F-NaF PET/CT and 18F-FDG PET/CT were studied. The findings of the three imaging procedures were compared for abnormal detection rates and concordance.

RESULTS

At post-therapy 131I WBS, all patients had skeletal involvement with a total of 21 bone iodine avid lesions. At 18F-FDG PET/TC, 19 bone lesions demonstrated increased tracer uptake and CT pathological alterations, while 2 lesions did not show any pathological finding. At 18F-NaF PET/CT, the 19 bone lesions detected at 18F-FDG PET/TC also demonstrated abnormal tracer uptake, and the other 2 bone iodine avid foci did not show any pathological finding.

CONCLUSIONS

In patients with DTC, 18F-NaF PET/CT did not obtain more information on the metastatic skeletal involvement than post-therapy 131I WBS and 18F-FDG PET/CT.

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.

Thyroid Cancer Radiomics: Navigating Challenges in a Developing Landscape

In a review from 2021 by Cao et al [...].

Prediction of placenta accreta spectrum in patients with placenta previa using a clinical, US and MRI combined model: A retrospective study with external validation

PURPOSE

To build and validate a predictive model of placental accreta spectrum (PAS) in patients with placenta previa (PP) combining clinical risk factors (CRF) with US and MRI signs.

METHOD

Our retrospective study included patients with PP from two institutions. All patients underwent US and MRI examinations for suspicion of PAS. CRF consisting of maternal age, cesarean section number, smoking and hypertension were retrieved. US and MRI signs suggestive of PAS were evaluated. Logistic regression analysis was performed to identify CRF and/or US and MRI signs associated with PAS considering histology as the reference standard. A nomogram was created using significant CRF and imaging signs at multivariate analysis, and its diagnostic accuracy was measured using the area under the binomial ROC curve (AUC), and the cut-off point was determined by Youden's J statistic.

RESULTS

A total of 171 patients were enrolled from two institutions. Independent predictors of PAS included in the nomogram were: 1) smoking and number of previous CS among CRF; 2) loss of the retroplacental clear space at US; 3) intraplacental dark bands, focal interruption of the myometrial border and placental bulging at MRI. A PAS-prediction nomogram was built including these parameters and an optimal cut-off of 14.5 points was identified, showing the highest sensitivity (91%) and specificity (88%) with an AUC value of 0.95 (AUC of 0.80 in the external validation cohort).

CONCLUSION

A nomogram-based model combining CRF with US and MRI signs might help to predict PAS in PP patients, with MRI contributing more than US as imaging evaluation.

Segmentation methods applied to MRI-derived radiomic analysis for the prediction of placenta accreta spectrum in patients with placenta previa

PURPOSE

To retrospectively evaluate the performance of different manual segmentation methods of placenta MR images for predicting Placenta Accreta Spectrum (PAS) disorders in patients with placenta previa (PP) using a Machine Learning (ML) Radiomics analysis.

METHODS

64 patients (n=41 with PAS and n= 23 without PAS) with PP who underwent MRI examination for suspicion of PAS were retrospectively selected. All MRI examinations were acquired on a 1.5 T using T2-weighted (T2w) sequences on axial, sagittal and coronal planes. Ten different manual segmentation methods were performed on sagittal placental T2-weighted images obtaining five sets of 2D regions of interest (ROIs) and five sets of 3D volumes of interest (VOIs) from each patient. In detail, ROIs and VOIs were positioned on the following areas: placental tissue, retroplacental myometrium, cervix, placenta with underneath myometrium, placenta with underneath myometrium and cervix. For feature stability testing, the same process was repeated on 30 randomly selected placental MRI examinations by two additional radiologists, working independently and blinded to the original segmentation. Radiomic features were extracted from all available ROIs and VOIs. 100 iterations of 5-fold cross-validation with nested feature selection, based on recursive feature elimination, were subsequently run on each ROI/VOI to identify the best-performing method to classify instances correctly.

RESULTS

Among the segmentation methods, the best performance in predicting PAS was obtained by the VOIs covering the retroplacental myometrium (Mean validation score: 0.761, standard deviation: 0.116).

CONCLUSION

Our preliminary results show that the VOI including the retroplacental myometrium using T2w images seems to be the best method when segmenting images for the development of ML radiomics predictive models to identify PAS in patients with PP.

Prediction of 2-[18F]FDG PET-CT SUVmax for Adrenal Mass Characterization: A CT Radiomics Feasibility Study

BACKGROUND

Indeterminate adrenal masses (AM) pose a diagnostic challenge, and 2-[¹⁸F]FDG PET-CT serves as a problem-solving tool. Aim of this study was to investigate whether CT radiomics features could be used to predict the 2-[¹⁸F]FDG SUVmax of AM.

METHODS

Patients with AM on 2-[¹⁸F]FDG PET-CT scan were grouped based on iodine contrast injection as CT contrast-enhanced (CE) or CT unenhanced (NCE). Two-dimensional segmentations of AM were manually obtained by multiple operators on CT images. Image resampling and discretization (bin number = 16) were performed. 919 features were calculated using PyRadiomics. After scaling, unstable, redundant, and low variance features were discarded. Using linear regression and the Uniform Manifold Approximation and Projection technique, a CT radiomics synthetic value (RadSV) was obtained. The correlation between CT RadSV and 2-[¹⁸F]FDG SUVmax was assessed with Pearson test.

RESULTS

A total of 725 patients underwent PET-CT from April 2020 to April 2021. In 150 (21%) patients, a total of 179 AM (29 bilateral) were detected. Group CE consisted of 84 patients with 108 AM (size = 18.1 ± 4.9 mm) and Group NCE of 66 patients with 71 AM (size = 18.5 ± 3.8 mm). In both groups, 39 features were selected. No statisticallyf significant correlation between CT RadSV and 2-[¹⁸F]FDG SUVmax was found (Group CE, r = 0.18 and p = 0.058; Group NCE, r = 0.13 and p = 0.27).

CONCLUSIONS

It might not be feasible to predict 2-[¹⁸F]FDG SUVmax of AM using CT RadSV. Its role as a problem-solving tool for indeterminate AM remains fundamental.

Autoimmune liver diseases and SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing coronavirus disease 2019 (COVID-19), can trigger autoimmunity in genetically predisposed individuals through hyperstimulation of immune response and molecular mimicry. Here we summarise the current knowledge about auto-immune liver diseases (AILDs) and SARS-CoV-2, focusing on: (1) The risk of SARS-CoV-2 infection and the course of COVID-19 in patients affected by AILDs; (2) the role of SARS-CoV-2 in inducing liver damage and triggering AILDs; and (3) the ability of vaccines against SARS-CoV-2 to induce autoimmune responses in the liver. Data derived from the literature suggest that patients with AILDs do not carry an increased risk of SARS-Cov-2 infection but may develop a more severe course of COVID-19 if on treatment with steroids or thiopurine. Although SARS-CoV-2 infection can lead to the development of several autoimmune diseases, few reports correlate it to the appearance of de novo manifestation of immune-mediated liver diseases such as autoimmune hepatitis (AIH), primary biliary cholangitis (PBC) or AIH/PBC overlap syndrome. Different case series of an AIH-like syndrome with a good prognosis after SARS-CoV-2 vaccination have been described. Although the causal link between SARS-CoV-2 vaccines and AIH cannot be definitively established, these reports suggest that this association could be more than coincidental.

MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer

AIM

To non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with ER+ HER2- invasive breast cancer (IBC) using dynamic contrast-enhanced (DCE) MRI-derived radiomics features extracted from primary tumor lesions and a ML algorithm.

MATERIALS AND METHODS

Pre-operative DCE-MRI of patients with IBC, no history of neoadjuvant therapy prior to MRI, and for which the ODXRS was available, were retrospectively selected from a public dataset. ODXRS was obtained on histological tumor samples and considered as positive if greater than 16 and 26 in patients aged under and over 50 years, respectively. Tumor lesions were manually annotated by three independent operators on DCE-MRI images through 3D ROIs positioning. Radiomic features were therefore extracted and selected using multistep feature selection process. A logistic regression ML classifier was then employed for the prediction of ODXRS.

RESULTS

248 patients were included, of which 87 with positive ODXRS. 166 (66%) patients were grouped in the training set, while 82 (33%) in the test set. A total of 1288 features was extracted. Of these, 1244 were excluded as 771, 82 and 391 were excluded as not stable (n = 771), not variant (n = 82), and highly intercorrelated (n = 391), respectively. After the use of recursive feature elimination with logistic regression estimator and polynomial transformation, 92 features were finally selected. In the training set, the logistic regression classifier obtained an overall mean accuracy of 60%. In the test set, the accuracy of the ML classifier was 63%, with a sensitivity of 80%, specificity of 43%, and AUC of 66%.

CONCLUSIONS

Radiomics and ML applied to pre-operative DCE-MRI in patients with IBC showed promises for the non-invasive prediction of ODXRS, aiding in selecting patients who will benefit from NAC.

Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases

In patients with colorectal liver metastasis (CRLMs) unsuitable for surgery, oncological treatments, such as chemotherapy and targeted agents, can be performed. Cross-sectional imaging [computed tomography (CT), magnetic resonance imaging (MRI), 18-fluorodexoyglucose positron emission tomography with CT/MRI] evaluates the response of CRLMs to therapy, using post-treatment lesion shrinkage as a qualitative imaging parameter. This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them. Consequently, there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients. Advanced quantitative imaging (diffusion-weighted imaging, perfusion imaging, molecular imaging) allows the in vivo evaluation of specific biological tissue features described as quantitative parameters. Furthermore, radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters. As a result, parametric analysis (PA) translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion, structural heterogeneity, cellularity, oxygenation, and glucose consumption. PA could be a potentially useful imaging marker for predicting CRLMs treatment response. This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.

The True Value of Quantitative Imaging for Adrenal Mass Characterization: Reality or Possibility?

The widespread use of cross-sectional imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), in the evaluation of abdominal disorders has significantly increased the number of incidentally detected adrenal abnormalities, particularly adrenal masses [...].

Second Primary Malignancies in Patients with Differentiated Thyroid Cancer after Radionuclide Therapy: A Retrospective Single-Centre Study

Second primary malignancies (SPM) are described as any primary, not synchronous, malignancy arising in a different anatomical district, with confirmed histological diagnosis. Age at diagnosis, previous non-thyroidal primary malignancy, and radioactive iodine (RAI) therapy have been proposed as independent risk factors for SPM. RAI therapy is a standard treatment for moderate-high risk differentiated thyroid cancer (DTC), and its effect on the development of SPM has become a critical topic in DTC treatment. The purpose of this retrospective single-center study was to investigate the occurrence and the possible association of non-thyroidal SPM diagnosed after DTC and RAI therapy in a cohort of 1326 consecutive DTC patients referred at our Institution for RAI treatment from 1993 to 2009. Eighty-nine patients with ages ≤ 18 years at the time of DTC diagnosis or with a follow-up of ≤12 months were excluded from the final analysis. All patients underwent a complete clinical and hematological follow-up every 6 months for a minimum of 12 months. During follow-up (mean 89 ± 73 months), 25 patients (2%) had an SPM diagnosis (mean 133 ± 73 months). The most common site of the second malignancy was the breast, accounting for 32% of all SPM, followed by colon-rectal cancer (16%), leukemia, and gynecological and kidney cancer (4%). At Cox univariable regression analysis, age at DTC diagnosis (p < 0.001), age ≥55 years (p < 0.001) and follow-up duration (p < 0.004) were associated with SPM onset, while no significant association was observed with the administered activity of radioiodine. In conclusion, our data suggest that the older a person gets, the more sharply the likelihood of developing additional diseases, such as PMS, increases. Similarly, for follow-up, the more a patient is followed up clinically over time, the higher the risk of new diagnoses increases.

Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges

Simple Summary

Radiomics could increase the value of medical images for oncologic patients, allowing for the identification of novel imaging biomarkers and building prediction models. Unfortunately, despite the many promises and encouraging findings, the translation of radiomics into clinical practice appears as a distant goal. Indeed, challenges such as generalizability and reproducibility are slowing down the process and must be faced with a rigorous and robust radiomic methodology. In this review, we turn the spotlight to the methodological complexity of radiomics, providing an outline of dos and don’ts aimed at facilitating state-of-the-art research.

Abstract

Imaging plays a crucial role in the management of oncologic patients, from the initial diagnosis to staging and treatment response monitoring. Recently, it has been suggested that its importance could be further increased by accessing a new layer of previously hidden quantitative data at the pixel level. Using a multi-step process, radiomics extracts potential biomarkers from medical images that could power decision support tools. Despite the growing interest and rising number of research articles being published, radiomics is still far from fulfilling its promise of guiding oncologic imaging toward personalized medicine. This is, at least partly, due to the heterogeneous methodological quality in radiomic research, caused by the complexity of the analysis pipelines. In this review, we aim to disentangle this complexity with a stepwise approach. Specifically, we focus on challenges to face during image preprocessing and segmentation, how to handle imbalanced classes and avoid information leaks, as well as strategies for the proper validation of findings.

Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal

PURPOSE

Ultrasound and magnetic resonance imaging are the imaging modalities of choice for placenta accrete spectrum (PAS) disorders assessment. Radiomics could further increase the value of medical images and allow to overcome the limitations linked to their visual assessment. Aim of this systematic review was to identify and appraise the methodological quality of radiomics studies focused PAS disorders applications.

METHOD

Three online databases (PubMed, Scopus and Web of Science) were searched to identify original research articles on human subjects published in English. For the qualitative synthesis of results, data regarding study design (e.g., retrospective or prospective), purpose, patient population (e.g., sample size), imaging modalities and radiomics pipelines (e.g., segmentation and feature extraction strategy) were collected. The appraisal of methodological quality was performed using the Radiomics Quality Score (RQS).

RESULTS

10 articles were finally included and analyzed. All were retrospective and MRI-powered. The majority included more than 100 patients (6/10). Four were prognostic (focused on either the prediction of bleeding volume or the prediction of needed management) while six diagnostic (PAS vs not PAS classification) studies. The median RQS was 8, with maximum and minimum respectively equal to 17/36 and - 6/36. Major methodological concerns were the lack of feature stability to multiple segmentation testing and poor data openness.

CONCLUSIONS

Radiomics studies focused on PAS disorders showed a heterogeneous methodological quality, overall lower than desirable. Furthermore, many relevant research questions remain unexplored. More robust investigations are needed to foster advancements in the field and possibly clinical translation.

David Against Goliath: Direct Comparison of Handheld Bowel Sonography and Magnetic Resonance Enterography for Diagnosis of Crohn's Disease

BACKGROUND

Diagnosis of Crohn's disease (CD) requires ileo-colonoscopy (IC) and cross-sectional evaluation. Recently, "echoscopy" has been used effectively in several settings, although data about its use for CD diagnosis are still limited. Our aim was to evaluate the diagnostic accuracy of handheld bowel sonography (HHBS) in comparison with magnetic resonance enterography (MRE) for CD diagnosis.

METHODS

From September 2019 to June 2021, we prospectively recruited consecutive subjects attending our third level IBD Unit for suspected CD. Patients underwent IC, HHBS, and MRE in random order with operators blinded about the result of the other procedures. Bivariate correlation between MRE and HHBS was calculated by Spearman coefficient (r). To test the consistency between MRE and HHBS for CD location and complications, the Cohen's k measure was applied.

RESULTS

Crohn's disease diagnosis was made in 48 out of 85 subjects (56%). Sensitivity, specificity, positive predictive values, and negative predictive values for CD diagnosis were 87.50%, 91.89%, 93.33%, and 85% for HHBS; and 91.67%, 94.59%, 95.65%, and 89.74% for MRE, without significant differences in terms of diagnostic accuracy (89.41% for HHBS vs 92.94% for MRE, P = NS). Magnetic resonance enterography was superior to HHBS in defining CD extension (r = 0.67; P < .01) with a better diagnostic performance than HHBS for detecting location (k = 0.81; P < .01), strictures (k = 0.75; P < .01), abscesses (k = 0.68; P < .01), and fistulas (k = 0.65; P < .01).

CONCLUSION

Handheld bowel sonography and MRE are 2 accurate and noninvasive procedures for diagnosis of CD, although MRE is more sensitive in defining extension, location, and complications. Handheld bowel sonography could be used as effective ambulatory (or out-of-office) screening tool for identifying patients to refer for MRE examination due to high probability of CD diagnosis.

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.

Impact of COVID-19 pandemic on 2-[18F]FDG PET/CT imaging work-flow in a single medical institution: comparison among the three Italian waves

PURPOSE

To compare the impact of COVID-19 pandemic on 2-[18F]FDG PET/CT imaging work-flow during the three waves in a medical institution of southern of Italy.

METHODS

We retrospectively reviewed the numbers and results of 2-[18F]FDG PET/CT studies acquired during the following three periods of the COVID-19 waves: 1) February 3-April 30, 2020; 2) October 15, 2020-January 15, 2021; and 3) January 18-April 16, 2021.

RESULTS

A total of 861 PET/CT studies in 725 patients (388 men, mean age 64 ± 4 years) was acquired during the three waves of COVID-19 pandemic. The majority (94%) was performed for diagnosis/staging (n = 300) or follow-up (n = 512) of neoplastic diseases. The remaining 49 studies (6%) were acquired for non-oncological patients. The distribution of number and type of clinical indications for PET/CT studies in the three waves were comparable (p = 0.06). Conversely, the occurrence of patients positive for COVID-19 infection progressively increased (p < 0.0001) from the first to third wave; in particular, patients with COVID-19 had active infection before PET/CT study as confirmed by molecular oro/nasopharyngeal swab.

CONCLUSION

Despite the restrictive medical measures for the emergency, the number of 2-[18F]FDG PET/CT studies was unchanged during the three waves guaranteeing the diagnostic performance of PET/CT imaging for oncological patients.

Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences

The aim of this study was to calculate MRI quantitative parameters extracted from chemical-shift (CS) and dynamic contrast-enhanced (DCE) T1-weighted (T1-WS) images of adrenal lesions (AL) with qualitative heterogeneous signal drop on CS T1-WS and compare them to those of AL with homogeneous or no signal drop on CS T1-WS. On 3 T MRI, 65 patients with a total of 72 AL were studied. CS images were qualitatively assessed for grouping AL as showing homogeneous (Group 1, n = 19), heterogeneous (Group 2, n = 23), and no (Group 3, n = 30) signal drop. Histopathology or follow-up data served as reference standard to classify AL. ROIs were drawn both on CS and DCE images to obtain adrenal CS signal intensity index (ASII), absolute (AWO), and relative washout (RWO) values. Quantitative parameters (QP) were compared with ANOVA analysis and post hoc Dunn’s test. The performance of QP to classify AL was assessed with receiver operating characteristic analysis. CS ASII values were significantly different among the three groups (p < 0.001) with median values of 71%, 53%, and 3%, respectively. AWO/RWO values were similar in Groups 1 (adenomas) and 2 (benign AL) but significantly (p < 0.001) lower in Group 3 (20 benign AL and 10 malignant AL). With cut-offs, respectively, of 60% (Group 1 vs. 2), 20% (Group 2 vs. 3), and 37% (Group 1 vs. 3), CS ASII showed areas under the curve of 0.85, 0.96, and 0.93 for the classification of AL, overall higher than AWO/RWO. In conclusion, AL with qualitative heterogeneous signal drop at CS represent benign AL with QP by DCE sequence similar to those of AL with homogeneous signal drop at CS, but different to those of AL with no signal drop at CS; ASII seems to be the only quantitative parameter able to differentiate AL among the three different groups.

Qualitative evaluation of MR images for assessing placenta accreta spectrum disorders in patients with placenta previa: A pilot validation study

PURPOSE

To validate a qualitative imaging method using magnetic resonance (MR) for predicting placental accreta spectrum (PAS) in patients with placenta previa (PP).

METHOD

Two MR imaging methods built in our previous experience was tested in an external comparable group of sixty-five patients with PP; these methods consisted of presence of at least one (Method 1) or two (Method 2) of the following abnormal MR imaging signs: intraplacental dark bands, focal interruption of myometrial border and abnormal placental vascularity. Three groups of radiologists with different level of expertise evaluated MR images: at least 5 years of experience in body imaging (Group 1); at least 10 (Group 2) or 20 (Group 3) years of experience in genito-urinary MR. While radiologists of Group 1 routinely evaluated MR images, those of Groups 2 and 3 used both Methods 1 and 2.

RESULTS

A significant (p < 0.005) difference was found between the diagnostic accuracy values of imaging evaluation performed by Group 3 using Method 1 (63%) and Method 2 (89%); of note, the accuracy of Method 2 by Group 3 was also significantly (p < 0.005) higher compared to that of both Methods 1 (46%) and 2 (63%) by Group 2 as well as to that of the routine evaluation by Group 1 (60%).

CONCLUSIONS

The qualitative identification of at least two abnormal MR signs (Method 2) represents an accurate method for predicting PAS in patients with PP particularly when this method was used by more experienced radiologists; thus, imaging expertise and methodology is required for this purpose.

Risk of primary breast cancer in patients with differentiated thyroid cancer undergoing radioactive iodine therapy: a systematic review and meta-analysis

PURPOSE

Although it has been proven that radioactive iodine (RAI) treatment is an effective and well-tolerated procedure in patients with differentiated thyroid cancer (DTC), there is still some concern regarding the risk of developing a second primary malignancy after RAI administration. We performed a systematic review and meta-analysis to investigate the risk of primary breast cancer in patients with DTC undergoing RAI therapy.

METHODS

A comprehensive literature search of the PubMed, Scopus, and Web of Science databases was conducted according to the PRISMA statement.

RESULTS

The final analysis included 14 studies accounting for a total of 200,247 patients with DTC (98,368 treated with RAI and 101,879 not treated with RAI). The relative risk of primary breast cancer in patients with DTC treated with RAI to those not treated with RAI among studies ranged from 0.45 to 2.55, the pooled relative risk was 0.83 (95% confidence interval, 0.70-0.99), and the heterogeneity was 71.5%.

CONCLUSION

The present meta-analysis indicates that patients with DTC treated with RAI do not have a higher risk of primary breast cancer compared to those not treated with RAI. These findings suggest that RAI therapy does not increase the risk of breast cancer.

Tailored versus fixed scan delay in contrast-enhanced abdominal multi-detector CT: An intra-patient comparison of image quality

PURPOSE

To perform anintra-patient comparison betweena single-pass protocol (SP) and a portal venous phase (PVP) by means ofboth quantitative and qualitative analysis of image quality.

METHODS

Forty patients (31 M; 9F; aged 20-77 years; BMI 23 ± 4 Kg/m²) underwent both a SP and a PVP using a 64-rows multi-detector CT with a median interval time of 56 days (range5-903). All patients underwent i.v. bolus injection (2.0 cc/sec) of 1.7 cc/Kg of a non ionic iodinated contrast-media (370 mgI/ml) with scan delays of 67 ± 8 and 90 s for the SP and the PVP, respectively. Signal- (SNR) and contrast-to-noise ratios (CNR) were calculated for most visceral organs and for both abdominal aorta (AA) and main portal vein (MPV). For qualitative analysis, reproduction of abdominal viscera and vascular structures was blindly evaluated and inter-observer agreement calculated by the weighted Cohen k-analysis.

RESULTS

Attenuation values (H.U.) of AA (232 ± 53vs180 ± 36) and MPV (215 ± 39vs187 ± 42) were significantly (p < 0.001) higher in the SP than in PVP, respectively. At qualitative analysis, reproduction of mostabdominal viscerawas also significantly sharper (p < 0.001) with the SP than the PVPwith inter-observer agreement scores (k)ranging from 0.60 to 0.88 for all but one imaging criteria.

CONCLUSIONS

As the SP resulted in a significantly higher vascular enhancement and in a sharper reproduction of most abdominal viscera, it may be better suited than a PVP for the CT evaluation of non traumatic acute abdomen.

A rare case of congenital aneurysm of the portal system at level of spleno-porto-mesenteric confluence complicated by massive thrombosis

Portal venous aneurysm is a rare and potential dangerous vascular pathology, which can result in thrombosis or rupture. It may be congenital or acquired. Acquired form can be related mainly to portal hypertension, chronic hepatic disease, and trauma. We present a peculiar case of a congenital aneurysm involving the hepatic portal system in nearly all its extra-hepatic components: the main portal trunk, the spleno-porto-mesenteric confluence and the distal segment of splenic, superior, and inferior mesenteric veins, in a 20-year-old male patient. The aneurysm was complicated by massive thrombosis in absence of further predisposing factors.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study

The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.

Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives

Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.

Prediction of placenta accreta spectrum in patients with placenta previa using clinical risk factors, ultrasound and magnetic resonance imaging findings

OBJECTIVES

To predict placental accreta spectrum (PAS) in patients with placenta previa (PP) evaluating clinical risk factors (CRF), ultrasound (US) and magnetic resonance imaging (MRI) findings.

METHODS

Seventy patients with PP were retrospectively selected. CRF were retrieved from medical records. US and MRI images were evaluated to detect imaging signs suggestive of PAS. Univariable analysis was performed to identify CRF, US and MRI signs associated with PAS considering histology as standard of reference. Receiver operating characteristic curve (ROC) analysis was performed, and the area under the curve (AUC) was calculated. Multivariable analysis was also performed.

RESULTS

At univariable analysis, the number of previous cesarean section, smoking, loss of the retroplacental clear space, myometrial thinning < 1 mm, placental lacunae, intraplacental dark bands (IDB), focal interruption of myometrial border (FIMB) and abnormal vascularity were statistically significant. The AUC in predicting PAS progressively increased using CRF, US and MRI signs (0.69, 0.79 and 0.94, respectively; p < 0.05); the accuracy of MRI alone was similar to that obtained combining CRF, US and MRI variables (AUC = 0.97) and was significantly higher (p < 0.05) than that combining CRF and US (AUC = 0.83). Multivariable analysis showed that only IDB (p = 0.012) and FIMB (p = 0.029) were independently associated with PAS.

CONCLUSIONS

MRI is the best modality to predict PAS in patients with PP independently from CRF and/or US finding. It is reasonable to propose the combined assessment of CRF and US as the first diagnostic level to predict PAS, sparing MRI for selected cases in which US findings are uncertain for PAS.

Ultrasound and shear-wave elastography patterns of COVID-19 mRNA vaccine-related axillary, supra and subclavicular lymphadenopathy

In this pictorial essay, we illustrate the ultrasound appearance of COVID-19 Pfizer-BioNTech vaccine-related lymph node abnormalities, which can occur at different stations ipsilateral to the site of vaccination, after either first or second vaccine dose and can represent a diagnostic dilemma when encountered in patients with underlying conditions. Typically, they appear as enlarged hypoechoic nodes with loss of fat hilum, increased hilar and cortical vascularization at color-Doppler, but low to intermediate cortical consistence at shear-wave elastography. Asymmetric or diffuse cortical thickening is also frequently encountered. They can be observed in patients without and with clinical symptoms, such as armpit pain, fever and fatigue.

Imaging prediction with ultrasound and MRI of long-term medical outcome in native liver survivor patients with biliary atresia after kasai portoenterostomy: a pilot study

PURPOSE

To comparatively assess the role of abdominal ultrasound (US) and magnetic resonance imaging (MRI) in predicting long-term medical outcome in native liver survivor patients with biliary atresia (BA) after Kasai portoenterostomy (KP).

METHODS

Twenty-four retrospectively enrolled patients were divided in two groups according to clinical and laboratory data at initial evaluation after KP (median follow-up = 9.7 years; range = 5-25 years) as with ideal (Group 1; n = 15) or non-ideal (Group 2; n = 9) medical outcome. All patients were re-evaluated for a period of additional 4 years using clinical and laboratory indices. US and MRI studies were qualitatively analyzed assessing imaging signs suggestive of chronic liver disease (CLD).

RESULTS

At re-evaluation, 6 patients (40%) of Group 1 changed their medical outcome in non-ideal (Group 1A); the other 9 patients (60%) remained stable (Group 1B); the mean time to change the medical outcome in non-ideal status at re-evaluation was 43.5 ± 2.3 months. The area under the ROC curve was 0.84 and 0.87 for US and MRI scores to predict long-term medical outcome with the best cut-off value score > 4 for both modalities (p = 0.89). In Group 2, 6 (67%) patients showed a clinical progression (Group 2A) with a mean time of 39.8 ± 3.8 months; in the other 3 (33%) patients, no clinical progression was observed (Group 2B).

CONCLUSION

In BA patients with ideal medical outcome after KP, US and MRI may both predict long-term outcome. US, non-invasive and widely available technique, should be preferred.

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

Objectives

We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML’s accuracy with that of a breast radiologist, and verify if the radiologist’s performance is improved by using ML.

Methods

Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar’s test.

Results

After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70–90%) with an AUC of 0.82 (95% CI = 0.70–0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist’s performance improved when using ML (80.2%), but not significantly (p = 0.508).

Conclusions

A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images.

Key Points

• Machine learning showed good accuracy in discriminating benign from malignant breast lesions

• The machine learning classifier’s performance was comparable to that of a breast radiologist

• The radiologist’s accuracy improved with machine learning, but not significantly

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08009-2.

MRI to assess deep myometrial invasion in patients with endometrial cancer:A multi-reader study to evaluate the diagnostic role of different sequences

OBJECTIVE

The identification of deep myometrial invasion (DMI) represents a fundamental aspect in patients with endometrial cancer (EC) for accurate disease staging. It can be detected on MRI using T2-weighted (T2-w), diffusion weighted (DWI) and dynamic contrast enhanced sequences (DCE). Aim of the study was to perform a multi-reader evaluation of such sequences to identify the most accurate and its reliability for the best protocol.

METHODS

In this multicenter retrospective study, MRI were independently evaluated by 4 radiologists (2 senior and 2 novice) with a sequence-based approach to identify DMI. The performance of the entire protocol was also evaluated. A comparison between the different sequences assessed by the same reader was performed using receiver operating curve and post-hoc analysis. Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-observer variability.

RESULTS

A total of 92 patients were included. The performance of the readers did not show significant differences among DWI, DCE and the entire protocol. For only one senior radiologist, who reached the highest diagnostic accuracy with the entire protocol (82,6 %), both DWI (p = 0,0197) and entire protocol (p = 0,0039) were found significantly superior to T2-w. The highest inter-observer agreement was obtained with the entire protocol by expert readers (ICC = 0,77).

CONCLUSIONS

For the detection of DMI, the performances of DWI and DCE alone and that of a complete protocol do not significantly differ, even though the latter ensures the highest reliability particularly for expert readers. In cases in which T2-w and DWI are consistent, an unenhanced protocol could be proposed.

State of the art in abdominal MRI structured reporting: a review

In the management of several abdominal disorders, magnetic resonance imaging (MRI) has the potential to significantly improve patient's outcome due to its diagnostic accuracy leading to more appropriate treatment choice. However, its clinical value heavily relies on the quality and quantity of diagnostic information that radiologists manage to convey through their reports. To solve issues such as ambiguity and lack of comprehensiveness that can occur with conventional narrative reports, the adoption of structured reporting has been proposed. Using a checklist and standardized lexicon, structured reports are designed to increase clarity while assuring that all key imaging findings related to a specific disorder are included. Unfortunately, structured reports have their limitations too, such as risk of undue report simplification and poor template plasticity. Their adoption is also far from widespread, and probably the ideal balance between radiologist autonomy and report consistency of has yet to be found. In this article, we aimed to provide an overview of structured reporting proposals for abdominal MRI and of works assessing its value in comparison to conventional free-text reporting. While for several abdominal disorders there are structured templates that have been endorsed by scientific societies and their adoption might be beneficial, stronger evidence confirming their imperativeness and added value in terms of clinical practice is needed, especially regarding the improvement of patient outcome.

MR-enterography in Crohn's disease: what MRE mural parameters are associated to one-year therapeutic management outcome?

OBJECTIVE

To investigate the association of mural parameters of MR-enterography (MRE) with one-year therapeutic management of Crohn's disease (CD) patients.

METHODS

CD patients, undergone MRE with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps between January 2017 and June 2018, were retrospectively enrolled. Extramural complications represented an exclusion criterion because of their potential influence on the intrinsic characteristic of the bowel wall. Two groups of patients were defined on the base of the therapeutic management adopted at 1-year follow-up: Medical-group and surgical-group. The following MRE parameters were evaluated: wall-thickening, longitudinal-extension, T2-fat-suppression-mural-signal, ulcers, mural-oedema, wall-enhancement-rate/pattern, DWI-scores, ADC-values, strictures.

RESULTS

70 CD patients were enrolled. 57/70 (81.4%) were included in Medical-group and 13/70 (18.6%) in Surgical-group. ADCmean and strictures resulted to be significantly (p < 0.01) different between the two groups. The ADCmean showed to be significantly associated to conservative management [p < 0.01; OR: 0.0003; 95% CI (0.00-0.13)], while the strictures to surgical management [p < 0.01; OR: 29.7; 95% CI (4.9-179.7)]. ROC curves for ADCmean showed that AUC was 0.717 [95% CI (0.607-0.810), p < 0.01] with an optimal cut-off value of 1.081 × 10^-3 mm² s^-1. A negative predictive value of 90.2% was observed associating ADCmean values > 1.081 × 10^-3 mm² s^-1 to conservative therapy. 13/17 (76%) strictures with an ADCmean > 1.081 × 10^-3 mm² s^-1 benefited of conservative therapy.

CONCLUSION

ADCmean values calculated on DWI-MRE may be associated to 1-year conservative medical therapy in patients with CD without extramural complications.

ADVANCES IN KNOWLEDGE

ADC maps may be proposed to select CD patients with a lower burden of mural active inflammatory cells and/or fibrosis benefiting of 1-year conservative treatment.

Pancreatic Neuroendocrine Tumors in Patients with Multiple Endocrine Neoplasia Type 1: Diagnostic Value of Different MRI Sequences

BACKGROUND

MRI is a useful imaging modality to assess the presence of pancreatic neuroendocrine tumors (PNETs), allowing repeat monitoring examinations in multiple endocrine neoplasia type 1 (MEN-1) patients.

OBJECTIVES

We aimed to compare the diagnostic accuracy of conventional MRI sequences to identify which sequence better depicts the presence of PNETs in MEN-1 patients.

METHOD

We performed a retrospective analysis of consecutive MEN-1 patients who underwent a conventional MRI protocol to monitor previously proven PNETs. MRI sequences T1-w chemical shift (CS), T2-w HASTE, fat-suppressed (FS) T2-w HASTE, diffusion-weighted imaging (DWI), and pre- and post-contrast FS T1-w sequences were independently analyzed by 2 experienced radiologists using a 3-grade score (no lesion, uncertain lesion, and certain lesion); lesion size and signal intensity were recorded. A Friedman ANOVA and a Wilcoxon pairwise test for the post hoc analysis were used. The sensitivity of each sequence was measured, and the results were analyzed with the χ2 test.

RESULTS

We included 21 patients with a total of 45 PNETs proven by histology, endoscopic ultrasonography-guided fine-needle aspiration, CT, and nuclear medicine studies. A statistically significant (p < 0.01) difference was observed in the detection performance of each MRI sequence, particularly between DWI (91%) and T2-w FS (85%) sequences in comparison to the others (T1-w CS, T2-w, and pre- and post-contrast FS T1-w, ≤56% for all); no significant (p = 0.5) difference was found between the detection performance of DWI and T2-w FS sequences. No correlation was observed between the qualitative score of each sequence and lesion tumor size.

CONCLUSIONS

DWI and T2-w FS sequences proved to be the most accurate in the detection of PNETs, thus suggesting a role for an abbreviated MRI protocol without contrast medium administration for monitoring MEN-1 patients.

False iodine-131 MIBG scintigraphy findings in adrenal tumors: correlation with MR imaging

In this study, we report our experience regarding the occurrence of false radionuclide findings in adrenal iodine-131 MIBG scintigraphy. We present a total of five patients in which nuclear images were false negative or positive in three and two cases, respectively, according to the standard radionuclide established criteria. In particular, the three cases of false-negative MIBG images consisted of two patients with necrotic or cystic pheochromocytomas (Cases 1 and 3) and a patient with a small pheochromocytoma (Case 2); the two cases of false-positive MIBG imaging consisted of a patient with an adenoma showing intense tracer uptake and of a large primary necrotic carcinoma with heterogeneous tracer concentration.

Ultrasound, shear-wave elastography, and magnetic resonance imaging in native liver survivor patients with biliary atresia after Kasai portoenterostomy: correlation with medical outcome after treatment

BACKGROUND

Biliary atresia (BA) is a rare obliterative cholangiopathy and Kasai portoenterostomy (KP) represents its first-line treatment; clinical and laboratory parameters together with abdominal ultrasound (US) are usually performed during the follow-up. Shear-wave elastography (SWE) is able to evaluate liver parenchyma stiffness; magnetic resonance imaging (MRI) has also been proposed to study these patients.

PURPOSE

To correlate US, SWE, and MRI imaging findings with medical outcome in patients with BA who are native liver survivors after KP.

MATERIAL AND METHODS

We retrospectively enrolled 24 patients. They were divided in two groups based on "ideal" (n = 15) or "non-ideal" (n = 9) medical outcome. US, SWE, and MRI exams were analyzed qualitatively and quantitatively for imaging signs suggestive of chronic liver disease (CLD).

RESULTS

Significant differences were found in terms of liver surface (P = 0.007) and morphology (P = 0.013), portal vein diameter (P = 0.012) and spleen size (P = 0.002) by US, liver signal intensity (P = 0.013), portal vein diameter (P = 0.010), presence of portosystemic collaterals (P = 0.042), and spleen size (P = 0.001) by MRI. The evaluation of portal vein diameter (moderate, κ = 0.44), of portosystemic collaterals (good, κ = 0.78), and spleen size (very good, κ = 0.92) showed the best agreement between US and MRI. A significant (P = 0.01) difference in liver parenchyma stiffness by SWE was also found between the two groups (cut-off = 9.6 kPa, sensitivity = 55.6%, specificity = 100%, area under the ROC curve = 0.82).

CONCLUSION

US, SWE, and MRI findings correlate with the medical outcome in native liver survivor patients with BA treated with KP.

Current trends of artificial intelligence in cancer imaging

In this editorial, we discussed the current research status of artificial intelligence (AI) in Oncology, reviewing the basics of machine learning (ML) and deep learning (DL) techniques and their emerging applications on clinical and imaging cancer workflow. The growing amounts of available “big data” coupled to the increasing computational power have enabled the development of computer-based systems capable to perform advanced tasks in many areas of clinical care, especially in medical imaging. ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions. DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks; convolutional neural networks are the most common DL models used in medical image analysis. AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications. In detail, we explored the emerging applications of AI in oncological risk assessment, lesion detection, characterization, staging, and therapy response. Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice. Nevertheless, AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.

Management of differentiated thyroid cancer through nuclear medicine facilities during Covid-19 emergency: the telemedicine challenge

Purpose

To investigate whether a telemedicine service (TMS) carried out during the Covid-19 pandemic impacted on management of patients with differentiated thyroid cancer (DTC).

Methods

We retrospectively reviewed the number and the findings of outpatient visits in DTC subjects referred between March 11, 2020, and May 31, 2020, during the Covid-19 pandemic at the Radiometabolic Unit of the University of Naples Federico II. Office visits scheduled in March and May 2020 were converted in teleconsultation reaching all patients planned for an in-ward access to advise them to use the TMS for all clinical necessity. The number and the findings of DTC patients evaluated by in-ward access in the corresponding period of 2019 were also assessed for direct comparison.

Results

The number of outpatient visits performed by TMS during the pandemic (n = 445) and by in-ward access in the corresponding period of 2019 (n = 525) was comparable with only 15% of outpatient evaluations missed.

Conclusions

Our findings demonstrate the utility of telemedicine tools to avoid the potential negative impact of interruption or postponement of diagnostic and/or therapeutic procedures. Therefore, investments in medical network system development, including the implementation of telehealth approaches, should be encouraged at national and international levels.

FDG-PET/CT imaging during the Covid-19 emergency: a southern Italian perspective

PURPOSE

To assess the impact of the Covid-19 pandemic on FDG-PET/CT work volume and to evaluate the occurrence of abnormal imaging findings suspicious or potentially diagnostic for interstitial pneumonia by Covid-19 infection in south Italy.

METHODS

We retrospectively reviewed the number and the findings of FDG-PET/CT studies acquired between February and April 2020 during the Covid-19 pandemic at the University of Napoli Federico II. The number and the findings of FDG-PET/CT studies acquired in the corresponding period of 2019 were also assessed for direct comparison.

RESULTS

The number of FDG-PET/CT studies performed during the pandemic (n = 299) and in the corresponding period of 2019 (n = 335) were comparable. The percentage of abnormal FDG-PET/CT findings, suspicious for interstitial pneumonia by Covid-19 infection, was significantly higher during the pandemic (9%) compared with that found in the corresponding period of 2019 (4%) (χ² 5.45, P = 0.02). No significant differences were observed in the distribution of Covid-19 reporting and data system (CO-RADS) classification and in the maximum standardized uptake value between the pandemic (2.6 ± 2.2) and the corresponding period of 2019 (3.2 ± 1.4). Of note, patients with abnormal imaging findings during the pandemic time had clinical data and/or laboratory tests negative for Covid-19 infection.

CONCLUSION

Despite the restrictive medical measures for the emergency, the number of FDG-PET/CT studies was unchanged during the pandemic compared with the previous year. Our findings also indicate that Covid-19 infection was contained in our series of patients from southern Italy.

A critical appraisal of the quality of 18F-FDG PET/CT guidelines in oncology using the AGREE II tool: A EuroAIM initiative

PURPOSE

to assess the quality of guidelines on clinical indications for the use of ¹⁸F-FDG PET/CT in oncology using the AGREE II tool.

METHOD

from March to May 2019, the current literature was searched to identify guidelines focused on clinical indications for the use of¹⁸F-FDG PET/CT in oncology. The quality of the selected guidelines was then assessed by four independent appraisers using the AGREE II tool, which is organized in six quality domains accounting for a total of 23 items. The agreement among appraisers was measured using the intraclass correlation coefficient (ICC) analysis.

RESULTS

four guidelines were selected. Of these, one resulted of high quality (mean score 86.5 %), another showed an average quality (mean score 61.8 %), and the remaining two proved to be of low quality (mean scores of 53.3 and 45.7, respectively). With the exception of the high-quality guideline, critical domains were Domain 2 "Stakeholder involvement" (total mean score 56.28 + 15.9), Domain 3 "Rigor of development" (total mean score 43.48 + 27.6), Domain 5 "Applicability" (total mean score 46.90 + 19.4) and Domain 6 "Editorial independence" (total mean score 50.55 + 35.7). ICC values ranged from 0.939 to 0.995, indicating very good agreement among the four appraisers.

CONCLUSIONS

a heterogeneous quality of guidelines dealing with clinical indications for the use of ¹⁸F-FDG PET/CT in oncology emerged from our analysis. Further efforts should be made to improve the reporting of the applied methodology as well as to promote the applicability of guidelines into clinical practice.

Atypical dermoid cyst of the ovary during pregnancy: A multi-modality diagnostic approach

We report a case of a sixth-month-pregnant 37-year-old woman with abdominal pain with the presence of a dermoid cyst of the left ovary. The diagnostic work-up required a multi-modality imaging approach. In particular, US and MR examinations were initially performed but resulted with an inconclusive outcome of a final diagnosis. Hence, a CT scan was successively used to formulate lesion characterization. Thus, integrated imaging approach would be recommended.

US and MR imaging findings to detect placental adhesion spectrum (PAS) in patients with placenta previa: a comparative systematic study

PURPOSE

To compare the performance US and MR in identifying placental adhesion spectrum (PAS) in placenta previa (PP) and to establish a potential method of image interpretation.

METHODS

US and MR examinations of 51 patients with PP were selected. The presence of imaging signs commonly used to detect PAS was assessed. Penalized logistic regression was performed considering histology as standard of reference; only signs statistically significant (p < 0.05) were considered for ROC and multivariate analysis. The probability of PAS according to the presence of US and/or MR signs was then assessed.

RESULTS

At univariate analysis, loss of retroplacental clear space, myometrial thinning (MT) and placenta lacunar spaces on US, intraplacental dark bands (IDBs), focal interruption of myometrial border (FIMB) and abnormal vascularity (AV) on MR were statistically significant (p < 0.01). Three diagnostic methods for PAS were then developed for both US and MR when at least one (Method 1), two (Method 2) or three (Method 3) imaging signs occurred, respectively. Method 2 for MR showed a significantly (p < 0.05) higher accuracy (91%) compared to the other methods. When MR IDBs and AV as well as IDBs and FIMB were present in combination with US MT the probability of PAS increased from 75 to 90% and from 80 to 91%, respectively.

CONCLUSION

MR demonstrated a higher diagnostic accuracy than US to detect PAS. However, since the combination of MR and US signs could improve the probability to detect PAS, a complementary diagnostic role of these techniques could be considered.

Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging

Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the “in vivo” evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.