UNASSIGNED: Accurately and promptly predicting the response of gastrointestinal stromal tumors (GISTs) to targeted therapy is essential for optimizing treatment strategies. However, some fractions of recurrent or metastatic GISTs present as non-FDG-avid lesions, limiting the value of [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) in treatment evaluation. This study evaluated the efficacy of [18F]F-fibroblast activation protein inhibitor (FAPI)-42 [18F]FAPI-42) PET/CT for assessing the treatment response in recurrent or metastatic GISTs, in comparison to [18F]FDG PET/CT and explores a model integrating PET/CT imaging and clinical parameters to optimize the clinical use of these diagnostic tools.
UNASSIGNED: Our retrospective analysis included 27 patients with recurrent or metastatic GISTs who underwent [18F]FAPI-42 PET/CT and [18F]FDG PET/CT at baseline before switching targeted therapy. Treatment response status was divided into a progression group (PG) and a non-progression group (NPG) based on the Response Criteria in Solid Tumors (RECIST) 1.1, according to the contrast-enhanced computed tomography (CT) scan at six months. [18F]FAPI-42 and [18F]FDG PET/CT parameters including the mean standardized uptake value (SUVmean), the standard uptake value corrected for lean body mass (SULpeak), the maximum standardized uptake value (SUVmax), tumor-to-blood pool SUV ratio (TBR), tumor-to-liver SUV ratio (TLR), metabolic tumor volume (MTV)/FAPI-positive tumor volume (GTV-FAPI), total lesion glycolysis (TLG)/FAPI-positive total lesion accumulation (TLF) were correlated with the response status to identify indicative of treatment response. The predictive performance of them was quantified by generating receiver operating characteristic curves (ROC), calibration curves, and cross-validation.
UNASSIGNED: A total of 110 lesions were identified in 27 patients. Compared with PG, NPG was associated with lower levels of TBR and SUVmean in FDG PET/CT (TBR-FDG, SUVmean-FDG; P=0.033 and P=0.038, respectively), with higher SULpeak and TLF in FAPI PET/CT (SULpeak-FAPI, TLF-FAPI; P=0.10 and P=0.049, respectively). The predictive power of a composite-parameter model, including TBR-FDG, SULpeak-FAPI, gene mutation, and type of targeted therapy [area under the curve (AUC) =0.865], was superior to the few-parameter models incorporating TBR-FDG (AUC =0.637, P<0.001), SULpeak-FAPI (AUC =0.665, P<0.001) or both (AUC =0.721, P<0.001).
UNASSIGNED: Both [18F]FAPI-42 PET/CT and [18F]FDG PET/CT have value in predicting the treatment response of recurrent or metastatic GISTs. And [18F]FAPI-42 PET/CT offers synergistic value when used in combination with [18F]FDG PET/CT. Notably, the nomogram generated from the model incorporating [18F]FAPI-42 PET/CT, [18F]FDG PET/CT parameters, gene mutation, and type of targeted therapy could yield more precise predictions of the response of recurrent metastatic GISTs.

OBJECTIVE: To create a simple model using standard BI-RADS® descriptors from pre-treatment B-mode ultrasound (US) combined with clinicopathological tumor features, and to assess the potential of the model to predict the presence of residual tumor after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients.
METHODS: 245 female BC patients receiving NAC between January 2017 and December 2019 were included in this retrospective study. Two breast imaging fellows independently evaluated representative B-mode tumor images from baseline US. Additional clinicopathological tumor features were retrieved. The dataset was split into 170 training and 83 validation cases. Logistic regression was used in the training set to identify independent predictors of residual disease post NAC and to create a model, whose performance was evaluated by ROC curve analysis in the validation set. The reference standard was postoperative histology to determine the absence (pathological complete response, pCR) or presence (non-pCR) of residual invasive tumor in the breast or axillary lymph nodes.
RESULTS: 100 patients (40.8%) achieved pCR. Logistic regression demonstrated that tumor size, microlobulated margin, spiculated margin, the presence of calcifications, the presence of edema, HER2-positive molecular subtype, and triple-negative molecular subtype were independent predictors of residual disease. A model using these parameters demonstrated an area under the ROC curve of 0.873 in the training and 0.720 in the validation set for the prediction of residual tumor post NAC.
CONCLUSIONS: A simple model combining standard BI-RADS® descriptors from pre-treatment B-mode breast US with clinicopathological tumor features predicts the presence of residual disease after NAC.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC).
Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC.
A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65).
We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram.
The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis.
The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

OBJECTIVE: TNF inhibitors (TNFi) comprise 5 products whose structure and signalling differ. An individual patient with a rheumatic disease may respond to one TNFi but not to another. In addition, 30-40% of the patients respond inadequately to TNFi. The different TNFi downstream signalling may lead to their clinical efficacy. Several reports showed that TNFi exhibited differential effects on Th17 cells. We analyzed the different TNFi effects on IL-17A expression in the peripheral blood mononuclear cells (PBMCs) of patients with rheumatic diseases in order to evaluate the predictive capability of responses in an ex-vivo setting.
METHODS: PBMCs were co-cultured with the different TNFi or medium (control), and IL-17A mRNA levels were analyzed by qPCR. IL-17A expression levels in response to 4 TNFi (except certolizumab pegol) were compared with control. IL-17A expression in the assay was correlated to the clinical responses. Assay sensitivity and specificity for distinguishing responders from non-responders was calculated by receiver-operating characteristic (ROC) analysis.
RESULTS: The results of a retrospective cohort of patients with rheumatic diseases (n = 82) correlated with their therapeutic responses to the different TNFi with 89.5% accuracy. The assay predicted the responses of a prospective cohort (n = 54) to specific TNFi with 79% accuracy.
CONCLUSIONS: This functional assay could assist in predicting the odds for response to TNFi therapy, indicating whether a given patient is likely to respond to a specific TNFi.

Hypoxia within the tumor microenvironment is a critical factor influencing the efficacy of immunotherapy, including immune checkpoint inhibition. Insufficient oxygen supply, characteristic of hypoxia, has been recognized as a central determinant in the progression of various cancers. The reemergence of evofosfamide, a hypoxia-activated prodrug, as a potential treatment strategy has sparked interest in addressing the role of hypoxia in immunotherapy response. This investigation sought to understand the kinetics and heterogeneity of tumor hypoxia and their implications in affecting responses to immunotherapeutic interventions with and without evofosfamide.
This study aimed to investigate the influence of hypoxia on immune checkpoint inhibition, evofosfamide monotherapy, and their combination on colorectal cancer (CRC). Employing positron emission tomography (PET) imaging, we developed novel analytical methods to quantify and characterize tumor hypoxia severity and distribution.
Murine CRC models were longitudinally imaged with [18F]-fluoromisonidazole (FMISO)-PET to quantify tumor hypoxia during checkpoint blockade (anti-CTLA-4 + and anti-PD1 +/- evofosfamide). Metrics including maximum tumor [18F]FMISO uptake (FMISOmax) and mean tumor [18F]FMISO uptake (FMISOmean) were quantified and compared with normal muscle tissue (average muscle FMISO uptake (mAvg) and muscle standard deviation (mSD)). Histogram distributions were used to evaluate heterogeneity of tumor hypoxia.
Severe hypoxia significantly impeded immunotherapy effectiveness consistent with an immunosuppressive microenvironment. Hypoxia-specific PET imaging revealed a striking degree of spatial heterogeneity in tumor hypoxia, with some regions exhibiting significantly more severe hypoxia than others. The study identified FMISOmax as a robust predictor of immunotherapy response, emphasizing the impact of localized severe hypoxia on tumor volume control during therapy. Interestingly, evofosfamide did not directly reduce hypoxia but markedly improved the response to immunotherapy, uncovering an alternative mechanism for its efficacy.
These results enhance our comprehension of the interplay between hypoxia and immune checkpoint inhibition within the tumor microenvironment, offering crucial insights for the development of personalized cancer treatment strategies. Non-invasive hypoxia quantification through molecular imaging evaluating hypoxia severity may be an effective tool in guiding treatment planning, predicting therapy response, and ultimately improving patient outcomes across diverse cancer types and tumor microenvironments. It sets the stage for the translation of these findings into clinical practice, facilitating the optimization of immunotherapy regimens by addressing tumor hypoxia and thereby enhancing the efficacy of cancer treatments.

Despite decades of research, the prognosis of high-grade pediatric brain tumors (PBTs) remains dismal; however, recent cases of favorable clinical responses were documented in clinical trials using oncolytic viruses (OVs). In the current study, we employed four different species of OVs: adenovirus Delta24-RGD, herpes simplex virus rQNestin34.5v1, reovirus R124, and the non-virulent Newcastle disease virus rNDV-F0-GFP against three entities of PBTs (high-grade gliomas, atypical teratoid/rhabdoid tumors, and ependymomas) to determine their in vitro efficacy. These four OVs were screened on 14 patient-derived PBT cell cultures and the degree of oncolysis was assessed using an ATP-based assay. Subsequently, the observed viral efficacies were correlated to whole transcriptome data and Gene Ontology analysis was performed. Although no significant tumor type-specific OV efficacy was observed, the analysis revealed the intrinsic biological processes that associated with OV efficacy. The predictive power of the identified expression profiles was further validated in vitro by screening additional PBTs. In summary, our results demonstrate OV susceptibility of multiple patient-derived PBT entities and the ability to predict in vitro responses to OVs using unique expression profiles. Such profiles may hold promise for future OV preselection with effective oncolytic potency in a specific tumor, therewith potentially improving OV responses.

Effective management of chronic kidney disease (CKD), a major health problem worldwide, requires accurate and timely diagnosis, prognosis of progression, assessment of therapeutic efficacy, and, ideally, prediction of drug response. Multiple biomarkers and algorithms for evaluating specific aspects of CKD have been proposed in the literature, many of which are based on a small number of samples. Based on the evidence presented in relevant studies, a comprehensive overview of the different biomarkers applicable for clinical implementation is lacking. This review aims to compile information on the non-invasive diagnostic, prognostic, and predictive biomarkers currently available for the management of CKD and provide guidance on the application of these biomarkers. We specifically focus on biomarkers that have demonstrated added value in prospective studies or those based on prospectively collected samples including at least 100 subjects. Published data demonstrate that several valid non-invasive biomarkers of potential value in the management of CKD are currently available.

OBJECTIVE: Combined treatment of ablation and chemoembolization for hepatocellular carcinoma represents a promising therapy to increase treatment efficacy and improve patient survival. The \"hug sign\" is a recently introduced radiological sign consisting in deposition of beads/contrast agent during transarterial chemoembolization in the hyperemic area surrounding the post-ablation volume, seen during intraprocedural unenhanced cone-beam CT, that may indicate intraprocedural success. Aim of our retrospective study was to analyze the usefulness of the \"hug sign\" at the intraprocedural unenhanced cone-beam CT as an early predictor of response to combined treatment, based on the hug sign angle.
METHODS: Between January 2017 and September 2021 all patients with hepatocellular carcinoma which underwent a combined treatment of thermal ablation followed by chemoembolization were enrolled. All treated patients underwent immediate post-procedural unenhanced cone-beam CT to evaluate the deposition of contrast agent, lipiodol or radiopaque beads and to assess the percentage of coverage of the ablated area with the contrast agent (hug sign angle). Patients with missing pre-procedural, intra-procedural and/or post-procedural data/imaging, or with poor-quality post-procedural cone-beam CT images were excluded.
RESULTS: 128 patients (mean age, 69.3 years ± 1.1 [standard deviation]; 87 men) were evaluated. Our study evidenced that 84.4% (81/85) of patients with a hug sign angle of 360° had no residual tumor at the first 1-/3-months follow-up examination. A hug sign angle of 360° also showed to be an independent protective factor against residual tumor at multivariate analysis.
CONCLUSIONS: Unenhanced cone-beam CT performed at the end of a combined treatment with ablation plus chemoembolization can effectively predict an early treatment response on radiological images, when a hug sign angle of 360° was detected.

Rectal cancer poses challenges in preoperative treatment response, with up to 30% achieving a complete response (CR). Personalized treatment relies on accurate identification of responders at diagnosis. This study aimed to unravel CR determinants, overall survival (OS), and time to recurrence (TTR) using clinical and targeted sequencing data. Analyzing 402 patients undergoing preoperative treatment, tumor stage, size, and treatment emerged as robust response predictors. CR rates were higher in smaller, early-stage, and intensively treated tumors. Targeted sequencing analyzed 216 cases, while 120 patients provided hotspot mutation data. KRAS mutation dramatically reduced CR odds by over 50% (odds ratio [OR] = 0.3 in the targeted sequencing and OR = 0.4 hotspot cohorts, respectively). In contrast, SMAD4 and SYNE1 mutations were associated with higher CR rates (OR = 6.0 and 6.8, respectively). Favorable OS was linked to younger age, CR, and low baseline carcinoembryonic antigen levels. Notably, CR and an APC mutation increased TTR, while a BRAF mutation negatively affected TTR. Beyond tumor burden, SMAD4 and SYNE1 mutations significantly influenced CR. KRAS mutations independently correlated with radiotherapy resistance, and BRAF mutations heightened recurrence risk. Intriguingly, non-responding tumors with initially small sizes carried a higher risk of recurrence. The findings, even if limited in addition to the imperfect clinical factors, offer insights into rectal cancer treatment response, guiding personalized therapeutic strategies. By uncovering factors impacting CR, OS, and TTR, this study underscores the importance of tailored approaches for rectal cancer patients. These findings, based on extensive analysis and mutation data, pave the way for personalized interventions, optimizing outcomes in the challenges of rectal cancer preoperative treatment.

Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain\'s structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.