Tomography

Aim: To investigate the application of anterior and posterior corneal higher-order aberrations (HOAs) in detecting keratoconus (KC) and suspect keratoconus (SKC). Method: A retrospective, case-control study evaluating non-ectatic (normal) eyes, SKC eyes, and KC eyes. The Sirius Scheimpfug (CSO, Italy) analyses was used to measure HOAs of the anterior and posterior corneal surfaces. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Results: Two-hundred and twenty eyes were included in the analysis (normal n = 108, SKC n = 42, KC n = 70). Receiver operating characteristic (ROC) curve analysis revealed a high predictive ability for anterior corneal HOAs parameters: the root mean square (RMS) total corneal HOAs, RMS trefoil, and RMS coma to detect keratoconus (AUC > 0.9 for all). RMS Coma (3, ±1) derived from the anterior corneal surface was the parameter with the highest ability to discriminate between suspect keratoconus and normal eyes (AUC = 0.922; cut-off > 0.2). All posterior corneal HOAs parameters were unsatisfactory in discriminating between SKC and normal eyes (AUC < 0.8 for all). However, their ability to detect KC was excellent with AUC of >0.9 for all except RMS spherical aberrations (AUC = 0.846). Conclusions: Anterior and posterior corneal higher-order aberrations can differentiate between keratoconus and normal eyes, with a high level of certainty. In suspect keratoconus disease, however, only anterior corneal HOAs, and in particular coma-like aberrations, are of value. Corneal aberrometry may be of value in screening for keratoconus in populations with a high prevalence of the disease. Full article

Background: The purpose of this study was to investigate and compare the image quality of low-concentration-iodine (240 mgI/mL) contrast media (CM) and high-concentration-iodine (320 mgI/mL) CM according to the radiation dose. Methods: A total of 366 CT examinations were examined. Based on an assessment of quantitative and qualitative parameters by two radiologists, the quality was compared between Group A (low-concentration-iodine CM) and Group B (high-concentration-iodine CM) images of thyroid gland, sternocleidomastoid muscle (SCM), internal jugular vein (IJV), and common carotid artery (CCA). Another subgroup analysis compared Group a, (using ≤90 kVp in Group A), and Group b, (using ≥100 kVp in Group B) for finding the difference in image quality when the tube voltage is lowered. Results: Image quality did not differ between Groups A and B or between Groups a and b. The signal-to-noise ratio and contrast-to-noise ratio were significantly higher for Group B than Group A for the thyroid gland, IJV, and CCA. No statistical differences were found in the comparison of all structures between Groups a and b. Conclusion: There was no significant difference in image quality based on CM concentration with variable radiation doses. Therefore, if an appropriate CT protocol is applied, clinically feasible neck CT images can be obtained even using low-concentration-iodine CM. Full article

Gangliogliomas are uncommon intracranial tumors that include neoplastic and abnormal ganglion cells, and show positive immunohistochemical staining for GFAP and syn. This type of lesion occurs more frequently in the temporal lobe than in other areas; they are extremely rare in the suprasellar region. To the best of our knowledge, including our case, 19 cases of GGs have been found in the suprasellar region. Among them, five tumors invaded the optic nerve, nine tumors invaded the optic chiasm, one tumor invaded the optic tract, and two tumors invaded the entire optic chiasmal hypothalamic pathway. In the present study, we describe the first case of suprasellar GGs arising from the third ventricle floor that was removed through the endoscopic endonasal approach. In addition, we summarize the clinical characteristics of GGs, such as age of onset, gender distribution, MRI signs, main clinical symptoms, and treatment methods for GG cases. Full article

Background Rosai-Dorfman disease (RDD), the massive lymphadenopathy characterized by the proliferation of sinus histiocytosis, is a relatively idiopathic benign disease with unknown etiology. We reported a rare case of thymic RDD detected by ¹⁸F-FDG PET/CT. A 23-year-old man with right-sided chest pain underwent ¹⁸F-FDG PET/CT scan, showing increased ¹⁸F-FDG uptake in an anterior mediastinal mass corresponding to a thymic lesion at an enhanced CT scan. The patient was referred to surgery with the clinical suspicion of thymic malignancy. The histological examination and immunohistochemical results confirmed RDD. Conclusions This was the first case report of RDD isolated to the thymus and initially presented with chest pain. Moreover, there was no characteristic painless neck lymphadenopathy at any stage of the disease course. Thus, for young patients with thymus mass, RDD should be considered a rare but possible diagnosis. Full article

Background: Lung cancer represents a heterogeneous group of neoplasms, with the highest frequency and mortality in both sexes combined. In a clinical scenario characterized by the widespread of multidetector-row spiral CT, core-needle biopsy under tomographic guidance is one of the main and safest methods to obtain tissue specimens, even though there are relatively high rates of pneumothorax (0–60% incidence) and pulmonary hemorrhage (4–27% occurrence rates). The aim of this retrospective study is to assess the diagnostic accuracy of CT-guided core-needle biopsy in the diagnosis of primary lung malignancies and to compare our results with evidence from the literature. Materials and Methods: Our analysis included 350 thoracic biopsies, performed from 2017 to 2022 with a 64-row CT guidance and 16/18 G needles mounted on a biopsy gun. We included in the final cohort all samples with evidence of primary lung malignancies, precursor lesions, and atypia, as well as inconclusive and negative diagnoses. Results: There was sensitivity of 90.07% (95% CI 86.05–93.25%), accuracy of 98.87% (95% CI 98.12–99.69%), positive predictive value of 100%, and negative value of 98.74% (95% CI 98.23–99.10%). Specificity settled at 100% (93.84–100%). The AUC was 0.952 (95% CI 0.924–0.972). Only three patients experienced major complications after the procedure. Among minor complications, longer distances from the pleura, the presence of emphysema, and the lower dimensions of the lesions were correlated with the development of pneumothorax after the procedure, while longer distances from the pleura and the lower dimensions of the lesions were correlated with intra-alveolar hemorrhage. Immunohistochemistry analysis was performed in 51% of true positive cases, showing TTF-1, CK7, and p40 expression, respectively, in 26%, 24%, and 10% of analyzed samples. Conclusions: The CT-guided thoracic core-needle biopsy is an extremely accurate and safe diagnostic procedure for the histological diagnosis of lung cancer, a first-level interventional radiology exam for peripheral and subpleural lesions of the lung, which is also able to provide adequate samples for advanced pathologic assays (e.g., FISH, PCR) to assess molecular activity and genetic sequencing. Full article

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet’s AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging. Full article

This study investigated the relationship between the severity of pneumonia based on chest CT findings and that of pancreatic steatosis assessed using an automated volumetric measurement of the CT fat volume fraction (CT-FVF) of the pancreas, using unenhanced three-dimensional CT in polymerase chain reaction (PCR)-confirmed COVID-19 patients. The study population consisted of 128 patients with PCR-confirmed COVID-19 infection who underwent CT examinations. The CT-FVF of the pancreas was calculated using a histogram analysis for the isolation of fat-containing voxels in the pancreas. The CT-FVF (%) of the pancreas had a significantly positive correlation with the lung severity score on CT (ρ = 0.549, p < 0.01). CT-FVF (%) of the pancreas in the severe pneumonia group was significantly higher than that of the non-severe pneumonia group (21.7% vs. 7.8%, p < 0.01). The area under the curve of CT-FVF (%) of the pancreas in predicting the severity of pneumonia on CT was calculated to be 0.82, with a sensitivity of 88% and a specificity of 68% at a threshold for the severity score of 12.3. The automated volumetric measurement of the CT-FVF of the pancreas using unenhanced CT can help estimate disease severity in patients with COVID-19 pneumonia based on chest CT findings. Full article

In the present study, we aimed to assess frontal ethmoidal cells by using segmentation 3D software to establish a possible correlation between volume variation and both gender and age, as well as a relationship with Keros classification. Helical computed tomography images were obtained from 71 patients for analysis, in which the agger nasi cell, supra agger cell, supra agger frontal cell, supra bulla frontal cell, supraorbital ethmoidal cell, supra bulla cell, and frontal septal cell were identified and segmented to obtain their volume. Significant differences in volume were found for age and gender regarding agger nasi cells (p-value = 0.017), supra agger cells (p-value < 0.001), and frontal septal cells (p-value = 0.049). In the frontal septal cells, an increase of one year in age reduced the volume by 0.309, on average. According to Keros classification, the mean volumes were 10.07 and 25.64, respectively, for types II and III, both being greater than that of type I. Extracting volumetric data by using segmentation software in agger nasi cells, supra agger cells, and frontal septal cells may be useful for obtaining additional information related to age, in addition to possibly contributing to elucidating the anatomical variations in the region and an identification forensic tool. Full article

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer. Full article

Background: The thoracic inlet of blunt trauma patients may have pathologies that can be diagnosed on cervical spine computed tomography (CT) but that are not evident on concurrent portable chest radiography (pCXR). This retrospective investigation aimed to identify the prevalence of thoracic inlet pathologies on cervical spine CT and their importance by measuring the diagnostic performance of pCXR and the predictive factors of such abnormalities. Methods: This investigation was performed at a level-1 trauma center and included CT and concurrent pCXR of 385 consecutive adult patients (280 men, mean age of 47.6 years) who presented with suspected cervical spine injury. CT and pCXR findings were independently re-reviewed, and CT was considered the reference standard. Results: Traumatic, significant nontraumatic and nonsignificant pathologies were present at 23.4%, 23.6% and 58.2%, respectively. The most common traumatic diagnoses were pneumothorax (12.7%) and pulmonary contusion (10.4%). The most common significant nontraumatic findings were pulmonary nodules (8.1%), micronodules (6.8%) and septal thickening (4.2%). The prevalence of active tuberculosis was 3.4%. The sensitivity and positive predictive value of pCXR was 56.67% and 49.51% in diagnosing traumatic and 8.89% and 50% in significant nontraumatic pathologies. No demographic or pre-admission clinical factors could predict these abnormalities. Conclusions: Several significant pathologies of the thoracic inlet were visualized on trauma cervical spine CT. Since a concurrent pCXR was not sensitive and no demographic or clinical factors could predict these abnormalities, a liberal use of chest CT is suggested, particularly among those experiencing high-energy trauma with significant injuries of the thoracic inlet. If chest CT is not available, a meticulous evaluation of the thoracic inlet in the cervical spine CT of blunt trauma patients is important. Full article

Atrial size is a predictor of cardiovascular mortality. Non-ECG-gated computed tomography pulmonary angiography (CTPA) is a common test for cardiopulmonary evaluation but normative values for biatrial volumes are lacking. We derived normal CT biatrial volumes using manual and semiautomated segmentation with contemporaneous transthoracic echocardiography (TTE) to confirm normal diastology. Thirty-five consecutive cases in sinus rhythm with no history of cardio-vascular, renal, or pulmonary disease and normal diastolic function were selected. Planimetric CTPA measurements were compared to TTE volumes measured using area length method. TTE and CTPA derived normal LAVi and RAVi were 27 + 5 and 20 + 6 mL/m², and 30 + 8 and 29 + 9 mL/m², respectively. Bland–Altman analysis revealed an underestimation of biatrial volumes by TTE. TTE-CT mean biases for LAV and RAV were −5.7 + 12.0 mL and −16.2 + 14.8 mL, respectively. The CT intraclass correlation coefficients (ICC 95% CI) for LA and RA volumes were 0.99 (0.96–1.00) and 0.96 (0.76–0.99), respectively. There was excellent correlation (p < 0.001) between the semiautomated and manual measurements for LA (r 0.99, 95% CI 0.98–0.99) and RA (r 0.99, 95% CI 0.99–1.00). Atrial volumetric assessment on CTPA is easy and reproducible and can provide additional metric in cardiopulmonary assessment. Full article

Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convolutional neural network (CNN) and recurrent neural network (RNN) that takes a series of short axis slices as input and predicts a series of slice levels as output. Each slice image was labeled as one of the following five classes: out-of-apical, apical, mid, basal, and out-of-basal levels. A variety of multi-class classification models were evaluated. When compared with the CNN-alone models, the cascaded CNN-RNN models resulted in higher mean F1-score and accuracy. In our implementation and testing of four different baseline networks with different combinations of RNN modules, MobileNet as the feature extractor cascaded with a two-layer long short-term memory (LSTM) network produced the highest scores in four of the seven evaluation metrics, i.e., five F1-scores, area under the curve (AUC), and accuracy. Our study indicates that the cascaded CNN-RNN models are superior to the CNN-alone models for the classification of short axis slice levels in cardiac cine MR images. Full article

Primary aldosteronism (PA) is the most frequent cause of secondary hypertension, associated with an increased risk of cardiovascular and cerebral disease, compared to essential hypertension. Therefore, it is mandatory to promptly recognize the disease and offer to the patient the correct diagnostic–therapeutic process in order to reduce new-onset cardiovascular events. It is fundamental to define subtype classification (unilateral or bilateral disease), in order to provide the best treatment (surgery for unilateral and medical treatment for bilateral disease). Here, we report five clinical cases of different subtypes of PA (patients with monolateral or bilateral PA, nondiagnostic AVS, allergy to iodinated contrast, and patients not suitable for surgery), with particular attention to the diagnostic–therapeutic process and the different approaches tailored to a single case. Since PA is a spectrum of various diseases, it needs a personalized diagnostic–therapeutic process, customized for the individual patient, depending on previous medical history, suitability for the surgery and patient’s preferences. Full article

Nonoperative management (NOM) is increasingly utilized for rectal cancer patients with a clinical complete response (cCR) following total neoadjuvant therapy (TNT). The objective of this pilot study was to determine whether FDG-PET/MRI alters clinical response assessments among stage I-III rectal cancer patients undergoing TNT followed by NOM, relative to MRI alone. This prospective study included 14 subjects with new rectal cancer diagnoses. Imaging consisted of FDG-PET/MRI for initial staging, post-TNT restaging, and surveillance during NOM. Two independent readers assessed treatment response on MRI followed by FDG-PET/MRI. Inter-reader differences were resolved by consensus review. The reference standard for post-TNT restaging consisted of surgical pathology or clinical follow-up. 7/14 subjects completed post-TNT restaging FDG-PET/MRIs. 5/7 subjects had evidence of residual disease and underwent total mesorectal excision; 2/7 subjects had initial cCR with no evidence of disease after 12 months of NOM. FDG-PET/MRI assessments of cCR status at post-TNT restaging had an accuracy of 100%, compared with 71% for MRI alone, as FDG-PET detected residual tumor in 2 more subjects. Inter-reader agreement for cCR status on FDG-PET/MRI was moderate (kappa, 0.56). FDG-PET provided added value in 82% (9/11) of restaging/surveillance scans. Our preliminary data indicate that FDG-PET/MRI can detect more residual disease after TNT than MRI alone, with the FDG-PET component providing added value in most restaging/surveillance scans. Full article

Nowadays, there is still no consensus on the most accurate PET radiopharmaceutical to early detect prostate cancer (PCa) relapse. A tailored radiotracer choice based on a specific patient’s profile could ensure prompt disease detection and an improvement in patients management. We aimed to compare the [¹⁸F]fluciclovine and [¹⁸F]fluorocholine PET/CT detection rate (DR) in PCa patients restaged for early biochemical recurrence (BCR), according to clinical and biochemical features. A cohort of 138 PCa patients with early BCR (mean age: 71 y, range: 50–87 y) were homogeneously randomized 1:1 to a [¹⁸F]fluciclovine or a [¹⁸F]fluorocholine PET/CT group. The respective PET/CT DR, according to per-patient and per-region analysis, and the impact of the biochemical, clinical, and histological parameters, were compared. The PSA cut-off values predictive of a positive scan were also calculated. Overall, the [¹⁸F]fluciclovine PET/CT DR was 64%, significantly higher than the [¹⁸F]fluorocholine PET/CT DR of 35% (p = 0.001). Similarly, in the per-region analysis, the [¹⁸F]fluciclovine PET/CT DR was 51% in the prostate region, significantly higher compared to 15% of [¹⁸F]fluorocholine (p < 0.0001). Furthermore, a statistically significant higher DR in per-patient and per-region (prostate/prostate bed) analysis was observed in the [¹⁸F]fluciclovine group for 0.5–1 ng/mL (p = 0.018, p = 0.049) and >1 ng/mL (p = 0.040, p < 0.0001) PSA values. A PSA of 0.45 ng/mL for [¹⁸F]fluciclovine and of 0.94 ng/mL for [¹⁸F]fluorocholine was identified as the optimal cut-off value in predicting a positive PET/CT scan. Our results demonstrated a better [¹⁸F]fluciclovine PET/CT DR compared to [¹⁸F]fluorocholine for restaging PCa patients in early BCR, particularly in the detection of locoregional recurrence. The significantly higher [¹⁸F]fluciclovine DR for low PSA values (PSA < 1 ng/mL) supports its use in this setting of patients. Full article

Evaluating the similarity between two entities such as primary and suspected metastatic lesions using quantitative dual-energy computed tomography (DECT) numbers may be useful. However, the criteria for the similarity between two entities based on DECT numbers remain unclear. We therefore considered the possibility that a similarity in DECT numbers within the same organ could provide suitable standards. Thus, we assumed that the variation in DECT numbers within a single organ is sufficiently minimal to be considered clinically equivalent. Therefore, the purpose of this preliminary study is to investigate the differences in DECT numbers within upper abdominal organs. This retrospective study included 30 patients with data from hepatic protocol DECT scans. DECT numbers of the following parameters were collected: (a, b) 70 and 40 keV CT values, (c) slope, (d) effective Z, and (e, f) iodine and water concentration. The agreement of DECT numbers obtained from two regions of interest in the same organ (liver, spleen, and kidney) were assessed using Bland–Altman analysis. The diagnostic ability of each DECT parameter to distinguish between the same or different organs was also assessed using receiver operating characteristic analysis. The 95% limits of agreement within the same organ exhibited the narrowest value range on delayed phase (DP) CT [(c) −11.2–8.3%, (d) −2.0–1.5%, (e) −11.3–8.4%, and (f) −0.59–0.62%]. The diagnostic ability was notably high when using differences in DECT numbers on portal venous (PVP) and DP images (the area under the curve of DP: 0.987–0.999 in (c)–(f)). Using the variability in DECT numbers in the same organ as a criterion for defining similarity may be helpful in making a differential diagnosis by comparing the DECT numbers of two entities. Full article

There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety’s tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF $\le$ $10\%$) and a high-risk group (LSF $>$ $10\%$). Results: In this patient cohort, LSF was positively correlated with QTM velocity $\left|\mathit{u}\right|$ (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety’s parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM $\left|\mathit{u}\right|$ (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety’s $K^{trans}$ (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and $V_e$(0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for $\left|\mathit{u}\right|$, 0.80 for $V_e$ and 0.74 for $K^{trans}$. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing. Full article

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast–enhanced magnetic resonance imaging (DCE–MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann–Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_{_ADC}, model_{_IVIM}, model_{_DCE}, model_{_DCE+ADC}, and model_{_DCE+IVIM}). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_{_DCE+IVIM} showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828–0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_{_ADC} (p = 0.014) and model_{_IVIM} (p = 0.033). The model_{_ADC} had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672–0.846) but was not significantly different from model_{_IVIM} (p = 0.168). The united quantitative model with DCE–MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated. Full article

Aim: To assess the relationship between [¹⁸F]FDG PET/CT, breast cancer gene (BRCA) status, and their prognostic role in patients with ductal breast cancer (DBC). Methods: Forty-one women were included. PET/CT semiquantitative parameters such as standardized uptake value (SUV) body weight max (SUVmax), SUV body weight mean (SUVmean), SUV lean body mass (SUVlbm), SUV body surface area (SUVbsa), metabolic tumor volume (MTV), total lesion glycolysis (TLG), ratio SUVmax/blood-pool (S-BP), and ratio SUVmax/liver (S-L) were also extracted. The relationship between these parameters, BRCA, and other clinicopathological features were evaluated. Kaplan–Meier, univariate, and multivariate analyses were performed to find independent prognosticators for progression free (PFS) and overall survival (OS). Results: Significant positive correlations between BRCA status and SUVmax (p-value 0.025), SUVlbm (p-value 0.016), and SUVbsa (p-value 0.018) were reported. Mean PFS was 53.90 months with relapse/progression of disease occurring in nine (22.0%) patients; mean OS was 57.48 months with death occurring in two (4.9%) patients. Survival curves revealed TLG, MTV, and BRCA status as prognosticator for PFS; BRCA was also a prognosticator for OS. Univariate and multivariate analyses did not confirm such insights. Conclusion: We reported a correlation between some PET/CT parameters and BRCA status; some insights on their prognostic role have been underlined. Full article