Advantages and Challenges of Total-Body PET/CT at a Tertiary Cancer Center: Insights from Sun Yat-sen University Cancer Center.

In recent decades, researchers worldwide have directed their efforts toward enhancing the quality of PET imaging. The detection sensitivity and image resolution of conventional PET scanners with a short axial field of view have been constrained, leading to a suboptimal signal-to-noise ratio. The advent of long-axial-field-of-view PET scanners, exemplified by the uEXPLORER system, marked a significant advancement. Total-body PET imaging possesses an extensive scan range of 194 cm and an ultrahigh detection sensitivity, and it has emerged as a promising avenue for improving image quality while reducing the administered radioactivity dose and shortening acquisition times. In this review, we elucidate the application of the uEXPLORER system at the Sun Yat-sen University Cancer Center, including the disease distribution, patient selection workflow, scanning protocol, and several enhanced clinical applications, along with encountered challenges. We anticipate that this review will provide insights into routine clinical practice and ultimately improve patient care.

Learning CT-free attenuation-corrected total-body PET images through deep learning.

OBJECTIVES: Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning. METHODS: Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements. RESULTS: The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images. CONCLUSION: Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices. CLINICAL RELEVANCE STATEMENT: The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up. KEY POINTS: • CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

The burgeoning importance of PIWI-interacting RNAs in cancer progression.

PIWI-interacting RNAs (piRNAs) are a class of small noncoding RNA molecules that specifically bind to piwi protein family members to exert regulatory functions in germ cells. Recent studies have found that piRNAs, as tissue-specific molecules, both play oncogenic and tumor suppressive roles in cancer progression, including cancer cell proliferation, metastasis, chemoresistance and stemness. Additionally, the atypical manifestation of piRNAs and PIWI proteins in various malignancies presents a promising strategy for the identification of novel biomarkers and therapeutic targets in the diagnosis and management of tumors. Nonetheless, the precise functions of piRNAs in cancer progression and their underlying mechanisms have yet to be fully comprehended. This review aims to examine current research on the biogenesis and functions of piRNA and its burgeoning importance in cancer progression, thereby offering novel perspectives on the potential utilization of piRNAs and piwi proteins in the management and treatment of advanced cancer.

PSMA PET/MR is a New Imaging Option for Identifying Glioma Recurrence and Predicting Prognosis.

BACKGROUND: Glioma is characterized by a high recurrence rate, while the results of the traditional imaging methods (including magnetic resonance imaging, MRI) to distinguish recurrence from treatment-related changes (TRCs) are poor. Prostate-specific membrane antigen (PSMA) (US10815200B2, Deutsches Krebsforschungszentrum, German Cancer Research Center) is a type II transmembrane glycoprotein overexpressed in glioma vascular endothelium, and it is a promising target for imaging and therapy. OBJECTIVE: The study aimed to assess the performance of PSMA positron emission tomography/ magnetic resonance (PET/MR) for diagnosing recurrence and predicting prognosis in glioma patients. MATERIALS AND METHODS: Patients suspected of glioma recurrence who underwent 18F-PSMA-1007 PET/MR were prospectively enrolled. Eight metabolic parameters and fifteen texture features of the lesion were extracted from PSMA PET/MR. The ability of PSMA PET/MR to diagnose glioma recurrence was investigated and compared with conventional MRI. The diagnostic agreement was assessed using Cohen κ scores and the predictive parameters of PSMA PET/MR were obtained. Kaplan-Meier method and Cox proportional hazard model were used to analyze recurrence- free survival (RFS) and overall survival (OS). Finally, the expression of PSMA was analyzed by immunohistochemistry (IHC). RESULTS: Nineteen patients with a mean age of 48.11±15.72 were assessed. The maximum tumorto- parotid ratio (TPRmax) and texture features extracted from PET and T1-weighted contrast enhancement (T1-CE) MR showed differences between recurrence and TRCs (all p <0.05). PSMA PET/MR and conventional MRI exhibited comparable power in diagnosing recurrence with specificity and PPV of 100%. The interobserver concordance was fair between the two modalities (κ = 0.542, p = 0.072). The optimal cutoffs of metabolic parameters, including standardized uptake value (SUV, SUVmax, SUVmean, and SUVpeak) and TPRmax for predicting recurrence were 3.35, 1.73, 1.99, and 0.17 respectively, with the area under the curve (AUC) ranging from 0.767 to 0.817 (all p <0.05). In grade 4 glioblastoma (GBM) patients, SUVmax, SUVmean, SUVpeak, TBRmax, TBRmean, and TPRmax showed improved performance of AUC (0.833-0.867, p <0.05). Patients with SUVmax, SUVmean, or SUVpeak more than the cutoff value had significantly shorter RFS (all p <0.05). In addition, patients with SUVmean, SUVpeak, or TPRmax more than the cutoff value had significantly shorter OS (all p <0.05). PSMA expression of glioma vascular endothelium was observed in ten (10/11, 90.9%) patients with moderate-to-high levels in all GBM cases (n = 6/6, 100%). CONCLUSION: This primitive study shows multiparameter PSMA PET/MR to be useful in identifying glioma (especially GBM) recurrence by providing excellent tumor background comparison, tumor heterogeneity, recurrence prediction and prognosis information, although it did not improve the diagnostic performance compared to conventional MRI. Further and larger studies are required to define its potential clinical application in this setting.

Tumor response assessment by measuring the single largest lesion per organ in advanced non-small cell lung cancer patients treated with PD-1/PD-L1 inhibitor.

Background: For Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST1.1), measuring up to two target lesions per organ is an arbitrary criterion. Objectives: We sought to compare response assessment using RECIST1.1 and modified RECIST1.1 (mRECIST1.1, measuring the single largest lesion per organ) in advanced non-small cell lung cancer (aNSCLC) patients undergoing anti-PD-1/PD-L1 monotherapy. Methods: Concordance of radiologic response categorization between RECIST1.1 and mRECIST1.1 was compared using the Kappa statistics. C-index was calculated to evaluate prognostic accuracy of radiologic response by the two criteria. The Kaplan-Meier method and Cox regression analysis were conducted for progression-free survival (PFS) and overall survival (OS). Results: Eighty-seven patients who had at least two target lesions in any organ per the RECIST1.1 were eligible for comparison analysis. Tumor response showed excellent concordance when measured using the RECIST1.1 and mRECIST1.1 (Kappa = 0.961). C-index by these two criteria was similar for PFS (0.784 versus 0.785) and OS (0.649 versus 0.652). Responders had significantly longer PFS and OS versus non-responders (p < 0.05), whichever criterion adopted. Radiologic response remained a significant predictor of PFS and OS in multivariate analysis (p < 0.05). Conclusion: The mRECIST1.1 was comparable to RECIST1.1 in response assessment among aNSCLC patients who received single-agent PD-1/PD-L1 inhibitor. The mRECIST1.1, with reduced number of lesions to be measured, may be sufficient and more convenient to assess antitumor activity in clinical practice.

Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy.

The immune and stromal contexture within the tumor microenvironment (TME) interact with cancer cells and jointly determine disease process and therapeutic response. We aimed at developing a risk scoring model based on TME-related genes of squamous cell lung cancer to predict patient prognosis and immunotherapeutic response. TME-related genes were identified through exploring genes that correlated with immune scores and stromal scores. LASSO-Cox regression model was used to establish the TME-related risk scoring (TMErisk) model. A TMErisk model containing six genes was established. High TMErisk correlated with unfavorable OS in LUSC patients and this association was validated in multiple NSCLC datasets. Genes involved in pathways associated with immunosuppressive microenvironment were enriched in the high TMErisk group. Tumors with high TMErisk showed elevated infiltration of immunosuppressive cells. High TMErisk predicted worse immunotherapeutic response and prognosis across multiple carcinomas. TMErisk model could serve as a robust biomarker for predicting OS and immunotherapeutic response.

Continuation of anti-PD-1 therapy plus physician-choice treatment beyond first progression is not associated with clinical benefit in patients with advanced non-small cell lung cancer.

Objective: Few data are available on the optimal treatment options after disease progression from first-line treatment of immune checkpoint inhibitors (ICIs) plus chemotherapy. This study aimed to describe the safety and efficacy of continuing ICIs beyond first progress disease (PD) in non-small cell lung cancer (NSCLC). Methods: Patients with NSCLC previously treated with first-line anti-PD-1 antibody plus platinum-doublet chemotherapy and hence had PD as per Response Evaluation Criteria in Solid Tumors v1.1 were enrolled. For the subsequent line, patients received physician's choice (PsC) with or without an anti-PD-1 antibody. The primary outcome was progression-free survival after second-line treatment (PFS2). Secondary outcomes included overall survival (OS) from the initiation of first-line treatment, post-second-progression survival (P2PS), overall response rate (ORR), disease control rate (DCR), and safety during second-line treatment. Results: Between July 2018 and January 2021, 59 patients were included. A total of 33 patients received a physician-decided second-line regimen plus ICIs (PsC plus ICIs group), and 26 patients did not continue ICIs (PsC group). There was no significant difference in PFS2 between the PsC plus ICIs group and the PsC group (median, 6.5 vs. 5.7 months, p = 0.46). median OS (28.8 vs. 29.2 months), P2PS (13.4 vs. 18.7 months), ORR (18.2% vs. 19.2%), and DCR (78.8% vs, 84.6%) were also similar between the two groups. No new safety signals were observed. Conclusion: In this real-world setting, patients treated with continued ICIs beyond their first disease progression did not experience clinical benefit but without compromising safety.

Thyroid function and associated mood changes after COVID-19 vaccines in patients with Hashimoto thyroiditis.

Context: Severe acute respiratory syndrome-coronavirus 2 (COVID-19) vaccines may incur changes in thyroid functions followed by mood changes, and patients with Hashimoto thyroiditis (HT) were suggested to bear a higher risk. Objectives: We primarily aim to find whether COVID-19 vaccination could induce potential subsequent thyroid function and mood changes. The secondary aim was to find inflammatory biomarkers associated with risk. Methods: The retrospective, multi-center study recruited patients with HT receiving COVID-19-inactivated vaccines. C-reactive proteins (CRPs), thyroid-stimulating hormones (TSHs), and mood changes were studied before and after vaccination during a follow-up of a 6-month period. Independent association was investigated between incidence of mood state, thyroid functions, and inflammatory markers. Propensity score-matched comparisons between the vaccine and control groups were carried out to investigate the difference. Results: Final analysis included 2,765 patients with HT in the vaccine group and 1,288 patients in the control group. In the matched analysis, TSH increase and mood change incidence were both significantly higher in the vaccine group (11.9% versus 6.1% for TSH increase and 12.7% versus 8.4% for mood change incidence). An increase in CRP was associated with mood change (p< 0.01 by the Kaplan-Meier method) and severity (r = 0.75) after vaccination. Baseline CRP, TSH, and antibodies of thyroid peroxidase (anti-TPO) were found to predict incidence of mood changes. Conclusion: COVID-19 vaccination seemed to induce increased levels and incidence of TSH surge followed by mood changes in patients with HT. Higher levels of pre-vaccine serum TSH, CRP, and anti-TPO values were associated with higher incidence in the early post-vaccine phase.

18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy.

RATIONALE AND OBJECTIVES: To develop a combined model incorporating the clinical and PET features for identifying patients with diffuse large B-cell lymphoma (DLBCL) at high risk of progression or relapse after first-line therapy, compared to International Prognostic Index (IPI) and Deauville score (DS) assessment. MATERIALS AND METHODS: 271 18F-FDG PET images with DLBCL were retrospectively collected and randomly divided into the training (n=190) and test dataset (n=81). All visible lesions were annotated. Baseline, end-of-treatment (EoT), and delta PET radiomics features were extracted. IPI model, the baseline clinical model group (MG), DS model, the combined clinical MG, the PET-based radiomics MG, and the combined MG were constructed to predict 2-year time to progression (2Y-TTP). For each MG, the cross-combination method was performed to generate 1680 candidate models based on three normalization methods, 20 features, 4 feature-selection methods, and 7 classifiers. The model achieving the highest AUC was selected as the best for each MG. Cox regression analysis was further performed. RESULTS: In the test set, the best combined model showed better discriminative power compared to IPI model, the best baseline clinical model, DS model, the best combined clinical model, and the best PET-based radiomics model (AUC 0.898 vs. 0.584, 0.695, 0.756, 0.824, 0.832; p < 0.001, 0.014, 0.018, 0.152, 0.042, respectively). The combined model was superior to other models for progression-free-survival prediction (C-index: 0.853 vs. 0.568, 0.666, 0.753, 0.808, 0.814, respectively). CONCLUSION: A combined model for identifying patients at high risk of progression or relapse after first-line therapy was constructed, superior to IPI and DS assessment.

Significant CT dose reduction of 2-[18F]FDG PET/CT in pretreatment pediatric lymphoma without compromising the diagnostic and staging efficacy.

OBJECTIVES: To compare the diagnostic and staging efficacy of PET/diagnostic-level CT (PET/DxCT) and PET/low-dose CT (PET/LDCT) in pretreatment pediatric lymphoma patients and to estimate the reduction of the CT effective dose in the PET/CT scan. METHODS: One hundred and five pediatric patients who underwent total-body PET/CT examination were enrolled and divided into the DxCT group (n = 47) and LDCT group (n = 58) according to their dose levels. The sensitivity, specificity, PPV, and NPV of PET/DxCT and PET/LDCT for detecting the involvement of lymph node, spleen, bone marrow, and other extranodal organs in pretreatment lymphoma were compared. ROC analysis was performed to evaluate the integral efficiency. The staging accuracies based on PET/DxCT and PET/LDCT were also evaluated. Dosimetry was calculated for DxCT and LDCT, and the reduction in the effective dose was estimated. RESULTS: In the diagnosis of nodal, splenic, bone marrow, and other extranodal involvement, the differences in sensitivity, specificity, PPV, and NPV between PET/LDCT and PET/DxCT were not significant (all p values ∈ [0.332, 1.000]). Both modalities had accuracies above 90% and the ROC analysis indicated good or high efficiency in diagnosing all patterns of lymphoma involvement. PET/LDCT and PET/DxCT each had a staging accuracy of 89.7% and 89.4%, respectively. LDCT had a comparable image quality score with DxCT, with a significant increase in noise (p < 0.001) and a 66.1% reduction in effective dose. CONCLUSIONS: PET/LDCT allowed for a 66.1% CT effective dose reduction compared to PET/DxCT in pediatric lymphoma patients without compromising the diagnostic and staging efficacy. KEY POINTS: • Pediatric lymphoma patients can benefit from a reduced effective dose of PET/CT. • This retrospective study showed that the diagnostic and staging efficacies of PET/low-dose CT are comparable to those of PET/diagnostic-level CT, both with satisfactory efficiency in diagnosing all patterns of lymphoma involvement. • PET/low-dose CT allowed for a 66.1% CT effective dose reduction compared to PET/diagnostic-level CT.

High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning.

Background: Lowering the dose for positron emission tomography (PET) imaging reduces patients' radiation burden but decreases the image quality by increasing noise and reducing imaging detail and quantifications. This paper introduces a method for acquiring high-quality PET images from an ultra-low-dose state to achieve both high-quality images and a low radiation burden. Methods: We developed a two-task-based end-to-end generative adversarial network, named bi-c-GAN, that incorporated the advantages of PET and magnetic resonance imaging (MRI) modalities to synthesize high-quality PET images from an ultra-low-dose input. Moreover, a combined loss, including the mean absolute error, structural loss, and bias loss, was created to improve the trained model's performance. Real integrated PET/MRI data from 67 patients' axial heads (each with 161 slices) were used for training and validation purposes. Synthesized images were quantified by the peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), structural similarity (SSIM), and contrast noise ratio (CNR). The improvement ratios of these four selected quantitative metrics were used to compare the images produced by bi-c-GAN with other methods. Results: In the four-fold cross-validation, the proposed bi-c-GAN outperformed the other three selected methods (U-net, c-GAN, and multiple input c-GAN). With the bi-c-GAN, in a 5% low-dose PET, the image quality was higher than that of the other three methods by at least 6.7% in the PSNR, 0.6% in the SSIM, 1.3% in the NMSE, and 8% in the CNR. In the hold-out validation, bi-c-GAN improved the image quality compared to U-net and c-GAN in both 2.5% and 10% low-dose PET. For example, the PSNR using bi-C-GAN was at least 4.46% in the 2.5% low-dose PET and at most 14.88% in the 10% low-dose PET. Visual examples also showed a higher quality of images generated from the proposed method, demonstrating the denoising and improving ability of bi-c-GAN. Conclusions: By taking advantage of integrated PET/MR images and multitask deep learning (MDL), the proposed bi-c-GAN can efficiently improve the image quality of ultra-low-dose PET and reduce radiation exposure.

Dynamic alteration in SULmax predicts early pathological tumor response and short-term prognosis in non-small cell lung cancer treated with neoadjuvant immunochemotherapy.

Introduction: Biomarkers predicting tumor response to neoadjuvant immunochemotherapy in non-small cell lung cancer (NSCLC) are still lacking despite great efforts. We aimed to assess the effectiveness of the immune PET Response Criteria in Solid Tumors via SULmax (iPERCIST-max) in predicting tumor response to neoadjuvant immunochemotherapy and short-term survival in locally advanced NSCLC. Methods: In this prospective cohort study, we calculated SULmax, SULpeak, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and their dynamic percentage changes in a training cohort. We then investigated the correlation between alterations in these parameters and pathological tumor responses. Subsequently, iPERCIST-max defined by the proportional changes in the SULmax response (△SULmax%) was constructed and internally validated using a time-dependent receiver operating characteristic (ROC) curve and the area under the curve (AUC) value. A prospective cohort from the Sun Yat-Sen University Cancer Center (SYSUCC) was also included for external validation. The relationship between the iPERCIST-max responsiveness and event-free survival in the training cohort was also investigated. Results: Fifty-five patients with NSCLC were included in this study from May 2019 to December 2021. Significant alterations in post-treatment SULmax (p < 0.001), SULpeak (p < 0.001), SULmean (p < 0.001), MTV (p < 0.001), TLG (p < 0.001), and tumor size (p < 0.001) were observed compared to baseline values. Significant differences in SULpeak, SULmax, and SULmean between major pathological response (mPR) and non-mPR statuses were observed. The optimal cutoff values of the SULmax response rate were -70.0% and -88.0% using the X-tile software. The univariate and multivariate binary logistic regression showed that iPERCIST-max is the only significant key predictor for mPR status [OR = 84.0, 95% confidence interval (CI): 7.84-900.12, p < 0.001]. The AUC value for iPERCIST-max was 0.896 (95% CI: 0.776-1.000, p < 0.001). Further, external validation showed that the AUC value for iPERCIST-max in the SYSUCC cohort was 0.889 (95% CI: 0.698-1.000, p = 0.05). Significantly better event-free survival (EFS) in iPERCIST-max responsive disease (31.5 months, 95% CI 27.9-35.1) than that in iPERCIST-max unresponsive disease (22.2 months, 95% CI: 17.3-27.1 months, p = 0.024) was observed. Conclusion: iPERCIST-max could better predict both early pathological tumor response and short-term prognosis of NSCLC treated with neoadjuvant immunochemotherapy than commonly used criteria. Furthermore, large-scale prospective studies are required to confirm the generalizability of our findings.

A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment.

BACKGROUND: Brain metastasis (BM) comprises the most common reason for crizotinib failure in patients with anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC). We hypothesize that its occurrence could be predicted by a computed tomography (CT)-based radiomics model, therefore, allowing for selection of enriched patient populations for prevention therapies. METHODS: A total of 75 eligible patients were enrolled from Sun Yat-sen University Cancer Center between June 2014 and September 2019. The primary endpoint was brain metastasis-free survival (BMFS), estimated from the initiation of crizotinib to the date of the occurrence of BM. Patients were randomly divided into two cohorts for model training (n = 51) and validation (n = 24), respectively. A radiomics signature was constructed based on features extracted from chest CT before crizotinib treatment. Clinical model was developed using the Cox proportional hazards model. Log-rank test was performed to describe the difference of BMFS risk. RESULTS: Patients with low radiomics score had significantly longer BMFS than those with higher, both in the training cohort (p = 0.019) and validation cohort (p = 0.048). The nomogram combining smoking history and the radiomics signature showed good performance for the estimation of BMFS, both in the training (concordance index [C-index], 0.762; 95% confidence interval [CI], 0.663-0.861) and validation cohort (C-index, 0.724; 95% CI, 0.601-0.847). CONCLUSION: We have developed a CT-based radiomics model to predict subsequent BM in patients with non-brain metastatic NSCLC undergoing crizotinib treatment. Selection of an enriched patient population at high BM risk will facilitate the design of clinical trials or strategies to prevent BM.

Reducing number of target lesions for RECIST1.1 to predict survivals in patients with advanced non-small-cell lung cancer undergoing anti-PD1/PD-L1 monotherapy.

OBJECTIVES: The Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 provides conventional and standardized response assessment for multiple solid tumors. We investigated the smallest number of target lesions that can be measured without compromising response categorization and survival prediction in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 monotherapy. MATERIAL AND METHODS: 125 aNSCLC patients with at least two measurable lesions undergoing PD-1/PD-L1 inhibitor treatment were retrospectively studied. Tumor measurements allowing up to two lesions per organ and five lesions in total were reviewed. Inter-individual agreement and κ values for inter-method concordance on response status were evaluated based on up to five target lesions versus the largest one through four lesions. C-index was calculated to evaluate the prognostic accuracy of response categorization based on the selected number of target lesions for predicting overall survival (OS). Cox regression analysis was conducted for survival analysis. RESULTS: The highly consistent response assignment (99.2%) could be obtained when measuring the largest two lesions versus up to five lesions. Using the largest two through four lesions produced κ values of 0.986, 1.000 and 1.000 for response assessment, values significantly higher than those obtained when measuring the largest single lesion (κ = 0.850). C-index for overall survival (OS) was similar when assessing the largest one through five lesions, ranging from 0.646 to 0.654. Cox regression analyses showed that radiological response significantly predicted OS, irrespective of the number of target lesions selected. CONCLUSIONS: Reducing the number of target lesions does not affect OS prediction in aNSCLC patients treated with anti-PD-1/PD-L1 therapy. Considering the high intra-individual and inter-method concordance, using the largest two lesions in total is proposed to assess response.

The prognostic value of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: comparison of visual Deauville criteria and a lesion-to-liver SUVmax ratio-based evaluation system.

PURPOSE: The aim of this study was to determine a better criterion for end-of-treatment PET (EoT-PET) assessment and prognostic evaluation of patients with diffuse large B cell lymphoma (DLBCL). METHOD: EoT-PET scans were assessed using the visual Deauville 5-point scale (5PS) and LLR, the maximum standard uptake value ratio between the lesion and the liver. The cutoff value of LLR was obtained by receiver operator characteristic curve analysis. Patient outcomes were compared using Kaplan-Meier survival analysis. Prognostic indexes of different criteria were compared. Multivariate Cox regression analysis was performed to evaluate the prognostic factors. RESULTS: Four hundred forty-nine newly diagnosed DLBCL patients who received rituximab-based immunochemotherapy were included, and the median follow-up duration was 41.4 months. Patients with Deauville score (DS) 4 displayed significantly longer PFS and OS compared with patients with DS 5 (both p < 0.001), and they had significantly shorter PFS (p < 0.01) but similar OS (p = 0.057) compared with patients with DS 1-3. The differences in PFS and OS between groups were all significant whether positive EoT-PET was defined as DS 4-5 or DS 5 (all p < 0.001). The optimal cutoff of LLR was 1.83, and both PFS and OS were significantly different between EoT-PET-positive and EoT-PET-negative patients as defined by the cutoff (both p < 0.001). LLR-based criterion displayed higher specificity, positive predictive value, and accuracy than 5PS-based criterion in the prediction of disease progression and death events. In the multivariate analysis, positive EoT-PET (as defined by LLR) was related to unfavorable PFS and OS (both p < 0.001). Additional treatment was not correlated with outcomes of EoT-PET-negative patients either defined by LLR or 5PS or EoT-PET-positive patients classified by 5PS, but it was the only beneficial factor for OS (p < 0.05) in EoT-PET-positive patients with LLR ≥ 1.83. CONCLUSION: The optimal cutoff of LLR may be superior to Deauville criteria in identifying low-risk DLBCL patients with negative EoT-PET after the first-line immunochemotherapy and sparing them the cost and toxicity of additional treatment.

Unsatisfied Reporting Quality of Clinical Trials Evaluating Immune Checkpoint Inhibitor Therapy in Cancer.

Background: More and more immune-oncology trials have been conducted for treating various cancers, yet it is unclear what the reporting quality of immune-oncology trials is,and characteristics associated with higher reporting quality. Objective: This study aims to evaluate the reporting quality of immune-oncology trials. Methods: The PubMed and Cochrane library were searched to identify all English publications of clinical trials assessing immunotherapy for cancer. Reporting quality of immune-oncology trials was evaluated by a quality score with 11 points derived from the Trial Reporting in Immuno-Oncology (TRIO) statement, which contained two parts: an efficacy score of 6 points and toxicity score of 5 point. Linear regression was used to identify characteristics associated with higher scores. Results: Of the 10,169 studies screened, 298 immune-oncology trial reports were enrolled. The mean quality score, efficacy score, and toxicity score were 6.46, 3.61, and 2.85, respectively. The most common well-reported items were response evaluation criteria (96.0%) and toxicity grade (98.7%), followed by Kaplan-Meier survival analyses (80.5%). Treatment details beyond progression (12.8%) and toxicity onset time and duration (7.7%) were poorly reported. Multivariate regression revealed that higher impact factor (IF) (IF >20 vs. IF <5, p < 0.001), specific tumor type (p = 0.018 for lung, p = 0.021 for urinary system, vs. pan cancer), and a certain kind of immune checkpoint blocking agent (p < 0.001 for anti-PD-1 or multiagents, vs. anti-CTLA-4) were independent predictors of higher-quality score. Similar independent predictive characteristics were revealed for high-efficacy score. Only IF >20 had a significant high-toxicity score (p < 0.001). Conclusion: Immune-oncology trial reports presented an unsatisfied quality score, especially in the reporting of treatment details beyond progression and toxicity onset time and duration. High IF journals have better reporting quality. Future improvement of trial reporting was warranted to the benefit-risk assessment of immunotherapy.

Baseline and early changes in circulating Serum Amyloid A (SAA) predict survival outcomes in advanced non-small cell lung cancer patients treated with Anti-PD-1/PD-L1 monotherapy.

BACKGROUND: Systemic inflammation plays an important role in carcinogenesis and is associated with overall survival in patients with different cancer types, including those treated with immune checkpoint blockade (ICB). Serum Amyloid A (SAA) is an acute-phase protein and a marker of persistent inflammation. We hypothesized that circulating SAA may predict outcomes in advanced non-small cell lung (aNSCLC) patients treated with PD-1/PD-L1 ICB. MATERIALS AND METHODS: This retrospective study included 91 aNSCLC patients who received anti-PD-(L)1 monotherapy in Sun Yat-sen University Cancer Center (Guangzhou, China) between August 2016 and June 2018. We examined the impact of circulating SAA at baseline and 8 (±2) weeks later on overall survival (OS). X-tile program was used to determine the cut-off values which optimized the significance of the split between Kaplan-Meier survival curves. Kaplan-Meier methodology and Cox regression analyses were conducted for survival analyses. RESULTS: The optimal cut-off value of baseline SAA for OS stratification was 137.6 mg/L. In univariate analysis, both high level of baseline SAA (hazard ratio [HR], 2.76; 95% confidence interval [CI], 1.47-5.18; P = 0.002) and lack of early SAA descent (HR, 1.51; 95% CI, 1.11-2.06; P = 0.009) were significantly associated with inferior OS. In multivariate analysis, gender, smoking status, performance status, liver metastasis, neutrophil-to-lymphocyte ratio, baseline SAA and early changes in SAA independently predicted OS (all with P < 0.05). A combined baseline SAA ≥ 137.6 mg/L and without early SAA descent identified a small cohort with remarkably worse OS (median, 3.2 months). CONCLUSIONS: Both high baseline and lack of early decline in circulating SAA are significantly associated with inferior outcomes in aNSCLC patients treated with PD-1/PD-L1 ICB. Combined these two SAA indexes provided improved risk stratification. The prognostic value of this simple, readily-available, and cost-effective biomarker warrants larger, prospective validation before definitive recommendation can be made.

Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?

OBJECTIVES: This work aimed to investigate whether quantitative radiomics imaging features extracted from ultrasound (US) can noninvasively predict breast cancer (BC) metastasis to axillary lymph nodes (ALNs). METHODS: Presurgical B-mode US data of 196 patients with BC were retrospectively studied. The cases were divided into the training and validation cohorts (n = 141 versus 55). The elastic net regression technique was used for selecting features and building a signature in the training cohort. A linear combination of the selected features weighted by their respective coefficients produced a radiomics signature for each individual. A radiomics nomogram was established based on the radiomics signature and US-reported ALN status. In a receiver operating characteristic curve analysis, areas under the curves (AUCs) were determined for assessing the accuracy of the prediction model in predicting ALN metastasis in both cohorts. The clinical value was assessed by a decision curve analysis. RESULTS: In all, 843 radiomics features per case were obtained from expert-delineated lesions on US imaging in this study. Through radiomics feature selection, 21 features were selected to constitute the radiomics signature for predicting ALN metastasis. Area under the curve values of 0.778 and 0.725 were obtained in the training and validation cohorts, respectively, indicating moderate predictive ability. The radiomics nomogram comprising the radiomics signature and US-reported ALN status showed the best performance for ALN detection in the training cohort (AUC, 0.816) but moderate performance in the validation cohort (AUC, 0.759). The decision curve showed that both the radiomics signature and nomogram displayed good clinical utility. CONCLUSIONS: This pilot radiomics study provided a noninvasive method for predicting presurgical ALN metastasis status in BC.