Case Series: ATRX Variants in Four Patients with Metastatic Pheochromocytoma.

Few reports have highlighted the rare presence of somatic ATRX variants in clinically aggressive, metastatic pheochromocytoma/paraganglioma (PCC/PGL); however, none have addressed detailed clinical presentation (including biochemistry and imaging) and management of these patients. Here, we address these clinical features and management based on four PCC patients with somatic ATRX variants from our National Institutes of Health PCC/PGL cohort. A total of 192 patients underwent exome sequencing (germline, somatic, or both), and four males were found to have somatic ATRX variants (with additional somatic VHL and FH oncogenic variants in patients 2 and 4, respectively). Per-lesion and per-patient comparisons were performed among functional imaging scans performed at the NIH. Biochemical phenotype and response to systemic treatment were evaluated. This mini-series supports prior studies showing aggressive/metastatic PCC in patients with somatic ATRX variants, as all developed widespread metastatic disease. All four PCC patients presented with noradrenergic biochemical phenotype, and some with significant elevation in 3-methoxytyramine. 18F-FDOPA PET/CT was found to be the superior functional imaging modality, with 100% lesion detection rate when compared to that of 68Ga-DOTATATE, 18F-FDG, 18F-FDA, and 123I-MIBG scans. While patients did not respond to chemotherapy or tyrosine kinase inhibitors, they responded to targeted radiotherapy using high-specific-activity 131I-MIBG (Azedra®) or 177Lu-DOTATATE (Lutathera®).

Ultrashort Oncologic Whole-Body [18F]FDG Patlak Imaging Using LAFOV PET.

Methods to shorten [18F]FDG Patlak PET imaging procedures ranging from 65-90 to 20-30 min after injection, using a population-averaged input function (PIF) scaled to patient-specific image-derived input function (IDIF) values, were recently evaluated. The aim of the present study was to explore the feasibility of ultrashort 10-min [18F]FDG Patlak imaging at 55-65 min after injection using a PIF combined with direct Patlak reconstructions to provide reliable quantitative accuracy of lung tumor uptake, compared with a full-duration 65-min acquisition using an IDIF. Methods: Patients underwent a 65-min dynamic PET acquisition on a long-axial-field-of-view (LAFOV) Biograph Vision Quadra PET/CT scanner. Subsequently, direct Patlak reconstructions and image-based (with reconstructed dynamic images) Patlak analyses were performed using both the IDIF (time to relative kinetic equilibrium between blood and tissue concentration (t*) = 30 min) and a scaled PIF at 30-60 min after injection. Next, direct Patlak reconstructions were performed on the system console using only the last 10 min of the acquisition, that is, from 55 to 65 min after injection, and a scaled PIF using maximum crystal ring difference settings of both 85 and 322. Tumor lesion and healthy-tissue uptake was quantified and compared between the differently obtained parametric images to assess quantitative accuracy. Results: Good agreement was obtained between direct- and image-based Patlak analyses using the IDIF (t* = 30 min) and scaled PIF at 30-60 min after injection, performed using the different approaches, with no more than 8.8% deviation in tumor influx rate value (Ki ) (mean difference ranging from -0.0022 to 0.0018 mL/[min × g]). When direct Patlak reconstruction was performed on the system console, excellent agreement was found between the use of a scaled PIF at 30-60 min after injection versus 55-65 min after injection, with 2.4% deviation in tumor Ki (median difference, -0.0018 mL/[min × g]; range, -0.0047 to 0.0036 mL/[min × g]). For different maximum crystal ring difference settings using the scan time interval of 55-65 min after injection, only a 0.5% difference (median difference, 0.0000 mL/[min × g]; range, -0.0004 to 0.0013 mL/[min × g]) in tumor Ki was found. Conclusion: Ultrashort whole-body [18F]FDG Patlak imaging is feasible on an LAFOV Biograph Vision Quadra PET/CT system without loss of quantitative accuracy to assess lung tumor uptake compared with a full-duration 65-min acquisition. The ultrashort 10-min direct Patlak reconstruction with PIF allows for its implementation in clinical practice.

Predicting Pathologic Complete Response in Locally Advanced Rectal Cancer with [68Ga]Ga-FAPI-04 PET, [18F]FDG PET, and Contrast-Enhanced MRI: Lesion-to-Lesion Comparison with Pathology.

Neoadjuvant therapy in patients with locally advanced rectal cancer (LARC) has achieved good pathologic complete response (pCR) rates, potentially eliminating the need for surgical intervention. This study investigated preoperative methods for predicting pCR after neoadjuvant short-course radiotherapy (SCRT) combined with immunochemotherapy. Methods: Treatment-naïve patients with histologically confirmed LARC were enrolled from February 2023 to July 2023. Before surgery, the patients received neoadjuvant SCRT followed by 2 cycles of capecitabine and oxaliplatin plus camrelizumab. 68Ga-labeled fibroblast activation protein inhibitor ([68Ga]Ga-FAPI-04) PET/MRI, [18F]FDG PET/CT, and contrast-enhanced MRI were performed before treatment initiation and before surgery in each patient. PET and MRI features and the size and number of lesions were also collected from each scan. Each parameter's sensitivity, specificity, and diagnostic cutoff were derived via receiver-operating-characteristic curve analysis. Results: Twenty eligible patients (13 men, 7 women; mean age, 60.2 y) were enrolled and completed the entire trial, and all patients had proficient mismatch repair or microsatellite-stable LARC. A postoperative pCR was achieved in 9 patients (45.0%). In the visual evaluation, both [68Ga]Ga-FAPI-04 PET/MRI and [18F]FDG PET/CT were limited to forecasting pCR. Contrast-enhanced MRI had a low sensitivity of 55.56% to predict pCR. In the quantitative evaluation, [68Ga]Ga-FAPI-04 change in SULpeak percentage, where SULpeak is SUVpeak standardized by lean body mass, had the largest area under the curve (0.929) with high specificity (sensitivity, 77.78%; specificity, 100.0%; cutoff, 63.92%). Conclusion: [68Ga]Ga-FAPI-04 PET/MRI is a promising imaging modality for predicting pCR after SCRT combined with immunochemotherapy. The SULpeak decrease exceeding 63.92% may provide valuable guidance in selecting patients who can forgo surgery after neoadjuvant therapy.

Maximum standardised uptake value of positron emission tomography as a predictor of oesophageal cancer outcomes.

OBJECTIVES: This study aimed to analyse the value of pre-operative 18F-fluorodeoxyglucose positron emission tomography (PET)-computed tomography that can predict tumour pathological complete response, tumour histology grade, overall survival, and recurrence-free survival in patients with locally advanced oesophageal squamous cell carcinoma who underwent neoadjuvant chemoradiotherapy (NCRT) followed by surgery. METHODS: We retrospectively reviewed the cases of patients with locally advanced oesophageal squamous cell carcinoma undergoing NCRT followed by surgery. Patients who did not undergo PET within 3 months of surgery were excluded. We set a pre-operative PET maximum standardised uptake value (SUVmax) of > 5 as the threshold and classified the patients into two groups. We analysed the tumour response and histology grade, and compared the overall survival and recurrence-free survival between the two groups. RESULTS: This cohort included 92 patients with oesophageal squamous cell carcinoma who underwent NCRT followed by surgery; 49 patients had a pre-operative PET SUVmax < 5, and 43 patients had a pre-operative PET SUVmax > 5. The patients' pre-operative PET SUVmax correlated with tumour histology, ypT stage, and tumour response. Patients with a pre-operative SUVmax < 5 had better 2-year-overall survival (78% vs. 62%, P < 0.05) and 2-year recurrence-free survival (62% vs. 34%, P < 0.05) than those with a pre-operative SUV > 5. CONCLUSIONS: Pre-operative SUVmax may be useful to predict tumour response, survival, and recurrence in patients with locally advanced oesophageal squamous cell carcinoma who undergo NCRT followed by surgery.

A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.

INTRODUCTION: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to its poor prognosis. Predicting refractory status could greatly inform treatment strategies, enabling early intervention. Various options are now available based on patient and disease characteristics. Supervised machine-learning techniques, which can predict outcomes in a medical context, appear highly suitable for this purpose. DESIGN: Retrospective monocentric cohort study. PATIENT POPULATION: Adult patients with a first diagnosis of DLBCL admitted to the hematology unit from 2017 to 2022. AIM: We evaluated in our Center five supervised machine-learning (ML) models as a tool for the prediction of primary refractory DLBCL. MAIN RESULTS: One hundred and thirty patients with Diffuse Large B-cell lymphoma (DLBCL) were included in this study between January 2017 and December 2022. The variables used for analysis included demographic characteristics, clinical condition, disease characteristics, first-line therapy and PET-CT scan realization after 2 cycles of treatment. We compared five supervised ML models: support vector machine (SVM), Random Forest Classifier (RFC), Logistic Regression (LR), Naïve Bayes (NB) Categorical classifier and eXtreme Gradient Boost (XGboost), to predict primary refractory disease. The performance of these models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, false positive rate, sensitivity, and F1-score to identify the best model. After a median follow-up of 19.5 months, the overall survival rate was 60% in the cohort. The Overall Survival at 3 years was 58.5% (95%CI, 51-68.5) and the 3-years Progression Free Survival was 63% (95%CI, 54-71) using Kaplan-Meier method. Of the 124 patients who received a first line treatment, primary refractory disease occurred in 42 patients (33.8%) and 2 patients (1.6%) experienced relapse within 6 months. The univariate analysis on refractory disease status shows age (p = 0.009), Ann Arbor stage (p = 0.013), CMV infection (p = 0.012), comorbidity (p = 0.019), IPI score (p<0.001), first line of treatment (p<0.001), EBV infection (p = 0.008) and socio-economics status (p = 0.02) as influencing factors. The NB Categorical classifier emerged as the top-performing model, boasting a ROC-AUC of 0.81 (95% CI, 0.64-0.96), an accuracy of 83%, a F1-score of 0.82, and a low false positive rate at 10% on the validation set. The eXtreme Gradient Boost (XGboost) model and the Random Forest Classifier (RFC) followed with a ROC-AUC of 0.74 (95%CI, 0.52-0.93) and 0.67 (95%CI, 0.46-0.88) respectively, an accuracy of 78% and 72% respectively, a F1-score of 0.75 and 0.67 respectively, and a false positive rate of 10% for both. The other two models performed worse with ROC-AUC of 0.65 (95%CI, 0.40-0.87) and 0.45 (95%CI, 0.29-0.64) for SVM and LR respectively, an accuracy of 67% and 50% respectively, a f1-score of 0.64 and 0.43 respectively, and a false positive rate of 28% and 37% respectively. CONCLUSION: Machine learning algorithms, particularly the NB Categorical classifier, have the potential to improve the prediction of primary refractory disease in DLBCL patients, thereby providing a novel decision-making tool for managing this condition. To validate these results on a broader scale, multicenter studies are needed to confirm the results in larger cohorts.

Automated early ovarian cancer detection system based on bioinformatics.

Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient's serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection.

Solitary Renal Metastases From Stage IA Primary Lung Adenocarcinoma With Co-Alteration of EGFR, RB1, and MAP3K1: A Case Report.

We report a case of 59-year-old female with solitary bilateral renal metastases after surgery of stage IA primary lung adenocarcinoma who underwent next-generation sequencing (NGS) of both lesions. The patient received right upper lobectomy and lymph node dissection, which revealed primary invasive lung adenocarcinoma (pT1cN0M0, stage IA3). Two years following this, positron emission tomography-computed tomography (PET/CT) revealed multiple masses in both kidneys without other distant metastases, and ultrasonography-guided puncture biopsy indicated the presence of metastatic lung adenocarcinoma. The NGS of both the primary and metastatic lesions revealed the co-alteration of epidermal growth factor receptor (EGFR), RB transcriptional corepressor 1 (RB1), and mitogen-activated protein kinase kinase 1 (MAP3K1), which is potentially associated with the risk of renal metastasis in early postoperative non-small cell lung cancer.

Prognostic value of [18F]FDG PET/CT in metastatic hormone-sensitive prostate cancer at initial diagnosis: a retrospective cohort study.

INTRODUCTION: This retrospective study aimed to evaluate the prognostic value of [18F]FDG parameters in patients with visceral and bone metastatic hormone-sensitive prostate cancer (mHSPC). PATIENTS AND METHODS: This analysis included the mHSPC patients who underwent [18F]FDG PET/CT at the initial diagnosis. Baseline characteristics were analyzed, and the uptake of [18F]FDG was quantified using SUVmax. Kaplan-Meier and Cox proportional hazard regression analysis were employed to evaluate the correlation between SUVmax and patient survival. RESULTS: Among the 267 patients enrolled, 90 (33.7%) presented with visceral metastases and 177 (66.3%) had bone metastases. The median follow-up for the visceral metastasis group was 35.5 months (IQR 26-53.8 months). The median overall survival for patients with lung, liver, or both metastases were 30, 21 and 17 months, respectively. Patients exhibiting higher [18F]FDG uptake in metastatic lesions experienced shorter overall survival (OS) in comparison to those with lower [18F]FDG uptake, both in the visceral metastases group (17 vs. 31 months, p = 0.002) and the bone metastases group (27.5 vs. 34.5 months, p < 0.001). Cox regression analysis further revealed that increased [18F]FDG uptake in metastatic lesions emerged as a significant risk factor in both OS and progression-free survival (PFS). In contrast, the variability in [18F]FDG uptake in primary lesions did not provide a reliable indicator for predicting prognosis. CONCLUSIONS: In mHSPC patients, higher [18F]FDG uptake in metastatic lesions indicates shorter survival and increased risk of disease progression. The [18F]FDG SUVmax in primary tumors did not show significant prognostic value. Our study underscores the unique prognostic potential of [18F]FDG PET/CT in mHSPC patients, highlighting its importance in the management of both bone and visceral metastases.

Outstanding increase in tumor-to-background ratio over time allows tumor localization by [89Zr]Zr-PSMA-617 PET/CT in early biochemical recurrence of prostate cancer.

BACKGROUND: Positron emission tomography/computed tomography (PET/CT) using prostate-specific membrane antigen (PSMA)-targeted radiotracers labeled with zirconium-89 (89Zr; half-life ~ 78.41 h) showed promise in localizing biochemical recurrence of prostate cancer (BCR) in pilot studies. METHODS: Retrospective analysis of 38 consecutive men with BCR (median [minimum-maximum] prostate-specific antigen 0.52 (0.12-2.50 ng/mL) undergoing [89Zr]Zr-PSMA-617 PET/CT post-negative [68Ga]Ga-PSMA-11 PET/CT. PET/CT acquisition 1-h, 24-h, and 48-h post-injection of a median (minimum-maximum) [89Zr]Zr-PSMA-617 tracer activity of 123 (84-166) MBq. RESULTS: [89Zr]Zr-PSMA-617 PET/CT detected altogether 57 lesions: 18 local recurrences, 33 lymph node metastases, 6 bone metastases in 30/38 men with BCR (78%) and prior negative conventional PSMA PET/CT. Lesion uptake significantly increased from 1-h to 24-h and, in a majority of cases, from 24-h to 48-h. Tumor-to-background ratios significantly increased over time, with absolute increases of 100 or more. No side effects were noted. After [89Zr]Zr-PSMA-617 PET/CT-based treatment, prostate-specific antigen concentration decreased in all patients, becoming undetectable in a third of patients. LIMITATIONS: retrospective, single center design; infrequent histopathological and imaging verification. CONCLUSION: This large series provides further evidence that [89Zr]Zr-PSMA-617 PET/CT is a beneficial imaging modality to localize early BCR. A remarkable increase in tumor-to-background ratio over time allows localization of tumor unidentified on conventional PSMA PET/CT.

Regularity and correlation analysis of regional lymph node metastasis in nonoperative patients with non-small cell lung cancer based on positron emission tomography/computed tomography images.

BACKGROUND: Definitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced, inoperable non-small cell lung cancer (NSCLC). Previous studies have mainly focused on examining local failure and recurrence patterns after surgery and the principles of lymph node metastasis (LNM) in surgical candidates with NSCLC. However, these studies were just only able to guide postoperative radiotherapy (PORT) and the patterns of LNM in patients with resected NSCLC was inadequate to represent that in locally advanced inoperable NSCLC patients for guiding target volume delineation of CCRT. In this study, we aimed to analyze the metastasis regularities and establish the correlations between different lymph node levels in NSCLC patients without any intervention using positron emission tomography/computed tomography (PET/CT) images. METHODS: Overall, 358 patients with N1-N3 NSCLC admitted in our hospital between 2018 and 2022 were retrospectively analyzed. The diagnosis of metastatic lymph nodes was reviewed and determined using the European Organization for Research and Treatment of Cancer standard and the standardized value of the PET/CT examination. Univariate and multivariate analysis were performed to investigate the correlations between the different levels were evaluated by using of the chi-square test and logistic regression model. RESULTS: The lymph nodes with the highest metastasis rates in patients with left lung cancer were in order as follows: 10L, 4L, 5, 4R, and 7; while in those with right lung cancer they were 10R, 4R, 7, 2R, and 1R. Notably, we found left lung patients were more likely to have contralateral hilar, mediastinal and supraclavicular lymph nodes involved, and the right lung group exhibited a higher propensity for ipsilateral mediastinum and supraclavicular lymph node invasion. Furthermore, correlation analysis revealed there were significant correlative patterns in the LNM across different levels. CONCLUSIONS: This study elucidated the patterns of primary LNM in patients with NSCLC who had not undergone surgery (without any treatment interventions) and the correlations between lymph node levels. These findings were expected to provide useful reference for target volume delineation in definitive concurrent chemoradiotherapy in locally advanced NSCLC patients.

Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizing 18F-deoxyglucose positron emission tomography imaging.

OBJECTIVE: To evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma. METHODS: A total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes. RESULTS: Statistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05). CONCLUSIONS: The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups.

MRI-based Zero Echo Time and Black Bone Pseudo-CT Compared with Whole-Body CT to Detect Osteolytic Lesions in Multiple Myeloma.

Background MRI is highly sensitive for assessing bone marrow involvement in multiple myeloma (MM) but does not enable detection of osteolysis. Purpose To assess the diagnostic accuracy, repeatability, and reproducibility of pseudo-CT MRI sequences (zero echo time [ZTE], gradient-echo black bone [BB]) in detecting osteolytic lesions in MM using whole-body CT as the reference standard. Materials and Methods In this prospective study, consecutive patients were enrolled in our academic hospital between June 2021 and December 2022. Inclusion criteria were newly diagnosed MM, monoclonal gammopathy of undetermined significance at high risk for MM, or suspicion of progressive MM. Participants underwent ZTE and BB sequences covering the lumbar spine, pelvis, and proximal femurs as part of 3-T whole-body MRI examinations, as well as clinically indicated fluorine 18 fluorodeoxyglucose PET/CT examination within 1 month that included optimized whole-body CT. Ten bone regions and two scores (categorical score = presence/absence of osteolytic lesion; semiquantitative score = osteolytic lesion count) were assessed by three radiologists (two experienced and one unfamiliar with pseudo-CT reading) on the ZTE, BB, and whole-body CT images. The accuracy, repeatability, and reproducibility of categorical scores (according to Gwet agreement coefficients AC1 and AC2) and differences in semiquantitative scores were assessed at the per-sequence, per-region, and per-patient levels. Results A total of 47 participants (mean age, 67 years ± 11 [SD]; 27 male) were included. In experienced readers, BB and ZTE had the same high accuracy (98%) in the per-patient analysis, while BB accuracy ranged 83%-100% and ZTE accuracy ranged 74%-94% in the per-region analysis. An increase of false-negative (FN) findings in the spine ranging from +17% up to +23%, according to the lumbar vertebra, was observed using ZTE (P < .013). Regardless of the region (except coxal bones), differences in the BB score minus the ZTE score were positively skewed (P < .021). Regardless of the sequence or region, repeatability was very good (AC1 ≥0.87 for all), while reproducibility was at least good (AC2 ≥0.63 for all). Conclusion Both MRI-based ZTE and BB pseudo-CT sequences of the lumbar spine, pelvis, and femurs demonstrated high diagnostic accuracy in detecting osteolytic lesions in MM. Compared with BB, the ZTE sequence yielded more FN findings in the spine. ClinicalTrials.gov Identifier: NCT05381077 Published under a CC BY 4.0 license. Supplemental material is available for this article.

Characteristics of 18F-FDG PET/CT in patients with Kimura's disease from China.

BACKGROUND: 'Kimura's disease (KD) is a rare chronic inflammatory disorder of unknown etiology and is difficult to diagnose due to poor clinical presentation and imaging features. Few studies on characteristics of 18F-FDG PET/CT of KD have been reported. This study aimed to observe the reliable characteristics and usefulness of 18F-FDG PET/CT for the evaluation of consecutive patients with KD. METHODS: The clinical data and 18F-FDG PET/CT imaging findings of 8 patients with pathologically confirmed KD were reviewed retrospectively.18F-FDG PET/CT images were evaluated visually and semiquantitatively by measuring the maximum standardized uptake value (SUVmax). The correlations between clinical data and 18F-FDG PET/CT features were analyzed by simple linear regression. RESULTS: This study included 7 males and one female ranging in age from 17 to 79 years. The longest diameter of lesions ranged from 0.8 cm to 4.8 cm, and regional or generalized lymphadenopathy was found in all 8 patients with eosinophilia, while subcutaneous masses and salivary gland involvement concurrently were found in 4 patients. 18F-FDG PET/CT revealed that these involved lesions had high 18F-FDG uptake with SUVmax > 2.5 (2.6 to 6.3). Moreover, the margin of the lesions was well defined in 6 cases and ill defined in 2 cases, and homogeneous density and 18F-FDG uptake were both found in all these lesions. There was negative correlation between eosinophils and SUVmax (R2 = 0.538). CONCLUSIONS: Kimura's disease should be considered when 18F-FDG PET/CT is characterized by homogeneous lesions of regional or generalized lymphadenopathy, accompanied with subcutaneous masses and salivary gland involvement concurrently, especially in patients with eosinophilia.

Investigation of the distribution of inguinal lymph nodes and delineation of the inguinal clinical target volume using 18F-FDG PET/CT.

OBJECTIVE: Radiotherapy is a crucial treatment modality for pelvic cancers, but uncertainties persist in defining the clinical target volume (CTV) for the inguinal lymphatic drainage region. Suboptimal CTV delineation may compromise treatment efficacy and result in subpar disease control. This study aimed to investigate and map the distribution of lymph node metastases (LNM) in the groin area to facilitate an improved and detailed CTV definition using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). METHODS: Inguinal LNM in patients with biopsy-proven pelvic malignancies were identified using 18F-FDG PET/CT scan. The longitudinally nearest axial plane was determined based on six typical bony landmarks, and the axial direction relative to the femoral artery of LNM was recorded. The distances from the LNM to the nearest edge of the femoral artery were measured on the axial plane. An optimal margin to cover 95% of LNM was estimated to develop contouring recommendations. RESULTS: In this study, 500 positive LNM were identified by 18F-FDG PET/CT among 185 patients with primary pelvic malignancies. Relative to the femoral artery, lymph nodes were distributed laterally (10:00-11:00, n = 35), anteriorly (12:00-1:00, n = 213), and medially (2:00-4: 00, n = 252). For CTV delineation, the recommended distances from the femoral artery on the SFH were lateral 19 mm, anterior 19 mm, and medial 25 mm; on the SGT were lateral 26 mm, anterior 20 mm, and medial 25 mm; on the SPS were lateral 28 mm, anterior 29 mm, and medial 26 mm; on the IPS were anterior 29 mm and medial 28 mm; on the IIT were anterior 27 mm and medial 27 mm; on the ILT were anterior 25 mm and medial 23 mm. Use interpolation to contour the area between six axial slices, including any radiographically suspicious LNM. CONCLUSIONS: Using 18F-FDG PET/CT, we investigated the distribution pattern of inguinal LNM and propose a more comprehensive guideline for inguinal CTV delineation.

Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning.

PURPOSE: We sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT deep features radiomics signature (DFR-signature). METHODS: 369 DLBCL patients from two medical centers were included in this study. Their PET and CT images were fused to construct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signature was constructed through an Automated machine learning (AutoML) model. Combined with clinical indexes from the Cox regression analysis, we constructed a combined model to predict the progression-free survival (PFS) and the overall survival (OS) of patients. In addition, the combined model was evaluated in the concordance index (C-index) and the time-dependent area under the ROC curve (tdAUC). RESULTS: A total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors performed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation cohort. CONCLUSIONS: DFR-signature constructed from multimodal images improved the classification accuracy of prognosis for DLBCL patients. Moreover, the constructed DFR-signature combined with NCCN-IPI exhibited excellent potential for risk stratification of DLBCL patients.

Tumour-informed liquid biopsies to monitor advanced melanoma patients under immune checkpoint inhibition.

Immune checkpoint inhibitors (ICI) have significantly improved overall survival in melanoma patients. However, 60% experience severe adverse events and early response markers are lacking. Circulating tumour DNA (ctDNA) is a promising biomarker for treatment-response and recurrence detection. The prospective PET/LIT study included 104 patients with palliative combined or adjuvant ICI. Tumour-informed sequencing panels to monitor 30 patient-specific variants were designed and 321 liquid biopsies of 87 patients sequenced. Mean sequencing depth after deduplication using UMIs was 6000x and the error rate of UMI-corrected reads was 2.47×10-4. Variant allele fractions correlated with PET/CT MTV (rho=0.69), S100 (rho=0.72), and LDH (rho=0.54). A decrease of allele fractions between T1 and T2 was associated with improved PFS and OS in the palliative cohort (p = 0.008 and p < 0.001). ctDNA was detected in 76.9% of adjuvant patients with relapse (n = 10/13), while all patients without progression (n = 9) remained ctDNA negative. Tumour-informed liquid biopsies are a reliable tool for monitoring treatment response and early relapse in melanoma patients with ICI.

Preoperative Prediction of Her-2 and Ki-67 Status in Gastric Cancer Using 18F-FDG PET/CT Radiomics Features of Visceral Adipose Tissue.

Aims/Background Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (Her-2) and Ki-67 expression levels. However, IHC is invasive and cannot reflect their expression status in real-time. This study aimed to build radiomics models based on visceral adipose tissue (VAT)'s 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging, and to evaluate the relationship between radiomics features of VAT and positive expression of Her-2 and Ki-67 in gastric cancer (GC). Methods Ninety patients with GC were enrolled in this study. 18F-FDG PET/CT radiomics features were calculated using the PyRadiomics package. Two methods were employed to reduce radiomics features. The machine learning models, logistic regression (LR), and support vector machine (SVM), were constructed and estimated by the receiver operator characteristic (ROC) curve. The correlation of outstanding features with Ki-67 and Her-2 expression status was evaluated. Results For the Ki-67 set, the area under of the receiver operator characteristic curve (AUC) and accuracy were 0.86 and 0.79 for the LR model and 0.83 and 0.69 for the SVM model. For the Her-2 set, the AUC and accuracy were 0.84 and 0.86 for the LR model and 0.65 and 0.85 for the SVM model. The LR model for Ki-67 exhibited outstanding prediction performance. Three wavelet transform features were correlated with Her-2 expression status (p all < 0.001), and one wavelet transform feature was correlated with the expression status of Ki-67 (p = 0.042). Conclusion 18F-FDG PET/CT-based radiomics models of VAT demonstrate good performance in predicting Her-2 and Ki-67 expression status in patients with GC. Radiomics features can be used as imaging biomarkers for GC.

Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.

The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed with computational methods that predict [18F]-FDG uptake by BAT from CT; earlier population studies pave the way for developing such methods by showing some correlation between the Hounsfield Unit (HU) of BAT in CT and the corresponding [18F]-FDG uptake in PET. In this study, we propose training convolutional neural networks (CNNs) to predict [18F]-FDG uptake by BAT from unenhanced CT scans in the restricted regions that are likely to contain BAT. Using the Attention U-Net architecture, we perform experiments on datasets from four different cohorts, the largest study to date. We segment BAT regions using predicted [18F]-FDG uptake values, achieving 23% to 40% better accuracy than conventional CT thresholding. Additionally, BAT volumes computed from the segmentations distinguish the subjects with and without active BAT with an AUC of 0.8, compared to 0.6 for CT thresholding. These findings suggest CNNs can facilitate large-scale imaging studies more efficiently and cost-effectively using only CT.

A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.

BACKGROUND: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease. METHODS: MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted. RESULTS: Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning. CONCLUSION: Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation.

Prostate tuberculosis mimicking malignancy on 18F-FDG PET/CT in a patient with diffuse large B-cell lymphoma: A case report.

BACKGROUND: Prostate tuberculosis (TB) is a rare and often underdiagnosed condition due to its nonspecific symptoms and imaging features, which can mimic malignancies on 18F-fluorodeoxyglucose positron emission tomography (PET) scans. This resemblance poses a challenge in differentiating TB from prostate cancer, especially in patients with preexisting tumors such as diffuse large B-cell lymphoma. The purpose of this study is to highlight the importance of considering TB in the differential diagnosis of patients with atypical imaging findings, even in the presence of known malignancies. CASE: We present a case of a 60-year-old man with diffuse large B-cell lymphoma who was initially misdiagnosed with a prostate tumor based on 18F-fluorodeoxyglucose PET/computed tomography scans. The subsequent ultrasound-guided prostate biopsy confirmed the presence of prostate TB, not malignancy. CONCLUSIONS: This case report underscores the critical role of considering TB as a potential diagnosis in patients with hematological tumors and atypical imaging results. It serves as a reminder for clinicians to exercise caution when interpreting PET/computed tomography scans and to incorporate TB into their differential diagnoses, thereby avoiding misdiagnosis and inappropriate treatment.