Accurate Assessment Of Response Research Articles

The Landmark Series: The Future of Pancreatic Cancer Clinical Trials.

Pancreatic cancer has a poor prognosis despite ongoing advances in systemic and multimodal therapies. This review analyzes recent progress and future directions in pancreatic cancer clinical trials, emphasizing the evolution from traditional approaches to a more personalized and biologically-driven treatment paradigm. While improvements in overall survival have been achieved through perioperative therapies, gaps remain in our understanding of optimal treatment strategies. Key questions include selection of specific chemotherapeutic agents, duration of preoperative therapy, the role of radiotherapy, and accurate and real-time assessment of response to therapy. Historically, pancreatic cancer clinical trials have been designed based on anatomic criteria, failing to account for the inherent biologic heterogeneity of this disease. The field is now moving towards a precision oncology approach, leveraging genomic and transcriptomic data to identify predictive biomarkers and personalize treatment selection. Novel clinical trial designs, such as platform and basket trials, are accelerating the evaluation of new therapeutic strategies and facilitating efficient patient selection, particularly in the context of new emerging targeted therapies such as KRAS inhibitors. Furthermore, implementation of dynamic response assessment techniques, such as circulating tumor DNA and radiomics, may inform treatment decision-making and improve prediction of long-term outcomes. By integrating these evolving strategies, the emerging clinical trial landscape has the potential to transform the treatment of pancreatic cancer and yield meaningful improvements in patient outcomes.

Model based on ultrasound and clinicopathological characteristics for early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.

Accurate assessment of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial for mitigating chemotherapy-related toxicity in patients who do not respond to the treatment. Conventional ultrasound (US) has become a pivotal method for evaluating treatment response due to its cost-effectiveness, convenience, and absence of ionizing radiation. The objective of this study was to develop a model combining US and clinicopathological characteristics at baseline, as well as US features after one cycle of NAC, to predict the pCR to NAC in BC. This retrospective study included 74 patients with invasive BC who underwent NAC from January 2022 to December 2023. Data from US and clinicopathological characteristics before NAC (pre-NAC) and US features after one cycle of NAC were collected from all patients. Univariate and multivariate analyses were used to screen the factors independently associated with pCR and to develop the prediction model. Receiver operating characteristic (ROC) curve analysis was performed, and the area under the curve (AUC), sensitivity, and specificity were calculated to assess the predictive efficiency. Four characteristics, including human epidermal growth factor receptor 2 (HER2)-positive [odds ratio (OR) 9.265; 95% confidence interval (CI): 1.617-53.095, P=0.012] and absence of posterior feature or posterior acoustic enhancement of the breast mass on the US pre-NAC (OR 9.435; 95% CI: 1.585-56.180, P=0.014), the maximum diameter reduction measured with the US (OR 1.081; 95% CI: 1.009-1.157, P=0.026), and the angular or spiculated margin of the breast lesion with the US after one cycle of NAC (OR 9.475; 95% CI: 1.247-71.969, P=0.030), were screened as independent predictors. The AUC, sensitivity, and specificity of the prediction model were 0.912, 90.0%, and 79.6%, respectively. US and clinicopathological characteristics at baseline and the US features after one cycle of NAC helped predict pCR for BC. The prediction model may enable early evaluation of the efficacy of treatment strategies and guide less invasive surgical options or personalized post-treatment plans.

BIOM-59. INDIVIDUALIZED TUMOR-SPECIFIC AMPLIFIED DNA JUNCTIONS FOR PLASMA-BASED MONITORING IN PATIENTS WITH HIGH-GRADE GLIOMAS

Abstract Accurate response assessment in patients with high-grade gliomas (HGGs) is complicated by post-treatment radiographic changes that are equivocal for treatment effect versus progression. Plasma-based liquid biopsy assays for circulating tumor DNA (ctDNA) have had limited sensitivity to date, due in part to the presence of the blood-brain barrier that limits the diffusion of tumor-derived analytes out of the central nervous system. To address this challenge, we developed a ctDNA plasma-based assay to detect individualized tumor-derived amplified junctions in patients with HGGs. These junctions occur due to large-scale chromosomal rearrangements, such as those associated with epidermal growth factor receptor (EGFR) or cyclin-dependent kinase 4 (CDK4) amplification. To identify these amplified junctions, whole genome sequencing was performed on resected tissue. Primers were designed to flank junction breakpoints and were then utilized on each patient’s plasma samples to detect the amplified junction via qPCR. Tumor-specific amplified junctions were robustly detected in plasma from 14/14 (100%) patients with IDH-mutant or wild-type grade 4 astrocytomas whose tumor genomes demonstrated amplification-related junctions, located at chromosomes 7, 11, or 12. In comparison, ctDNA could not be detected in three control cases that did not have an amplification or did not have high copy number junctions. Importantly, serial plasma samples obtained prior to and at disease recurrence in 4 patients demonstrated an increasing abundance of amplified junctions that accurately correlated with disease progression in 4/4 patients (100%). In two of these patients, increasing abundance of amplified junctions preceded radiographic changes compatible with disease progression by approximately 2 or 4 months, respectively. In conclusion, tumor-specific amplified junctions could reliably be detected in plasma from patients with IDH-mutant or wild-type grade 4 astrocytomas and correlated with changes in disease burden. Further work is currently ongoing to evaluate these promising results in a larger cohort of patients with serial plasma samples.

Monitoring clonal burden as an alternative to blast count for myelodysplastic neoplasm treatment response.

Accurate assessment of therapy response in myelodysplastic neoplasm (MDS) has been challenging. Directly monitoring mutational disease burden may be useful, but is not currently included in MDS response criteria, and the correlation of mutational burden and traditional response endpoints is not completely understood. Here, we used genome-wide and targeted next-generation sequencing (NGS) to monitor clonal and subclonal molecular disease burden in 452 samples from 32 patients prospectively treated in a clinical trial. Molecular responses were compared with International Working Group (IWG) 2006-defined response assessments. We found that myeloblast percentage consistently underestimates MDS molecular disease burden and that mutational clearance patterns for marrow complete remission (mCR), which depends on myeloblast assessment, was not different than stable disease or bone marrow aplasia, underscoring a major limitation of using mCR. In contrast, achieving a complete remission (CR) was associated with the highest level of mutation clearance and lowest residual mutational burden in higher-risk MDS patients. A targeted gene panel approach was inferior to genome-wide sequencing in defining subclones and their molecular responses but may be adequate for monitoring molecular disease burden when a targeted gene is present in the founding clone. Our work supports incorporating serialNGS-based monitoring into prospective MDS clinical trials.

B-342 Teclistamab interference in serum protein electrophoresis and serum immunofixation electrophoresis in patients with recurrent or relapsed multiple myeloma

Abstract Background Serum protein electrophoresis (SPEP) and serum immunofixation electrophoresis (IFE) analyses are mainstays in the initial diagnosis, risk stratification, and evaluating treatment response in patients with multiple myeloma (MM) in accordance with the International Myeloma Working Group criteria. Published reports have demonstrated that therapeutic monoclonal antibodies (mAbs) used to treat MM (e.g. anti-CD38 IgG-kappa) are detectable by SPEP and IFE, which may interfere with accurate evaluation of complete response. Recently, Teclistamab (Tecvayli®; Janssen Biotech, Horsham Township, PA), a humanized bispecific IgG-lambda mAb targeting B-cell maturation antigen (BCMA) and CD3 on T cells was approved for treating patients with penta-refractory MM. In contrast with other IgG-kappa therapeutic mAbs, the degree to which Teclistamab may interfere with detection and quantification of monoclonal proteins is unknown. Therefore, we sought to assess the interference of Teclistamab therapy on routine SPEP and IFE analysis. Methods Patients treated with Teclistamab were identified by retrospective review of electronic medical records. Teclistamab (90 mg/mL) was spiked into a remnant patient sample with IgG lambda MM at a final concentration of 10 μg/mL, which corresponds to ½Cmax values in patients infused with Teclistamab. SPEP and IFE were performed using the Helena SPIFE Touch instrument. Computer-assisted densitometry was used to quantify monoclonal proteins in the gamma region of SPEP gels. Results In several patients with refractory MM treated with Teclistamab, IFE analysis revealed IgG lambda or lambda light chain monoclonal proteins, which were inconsistent with previously-identified monoclonal proteins (e.g. IgG kappa). In one patient diagnosed with IgG lambda MM, spiking 10 μg/mL Teclistamab increased M-spike abundance by approximately 0.15 g/dL when quantified by SPEP. Conclusions In patients with relapsed or refractory MM, treatment with Teclistamab may confound accurate clinical assessment of complete response by SPEP and IFE methods. Increased awareness of this preanalytical interference is necessary to guide clinical decision making.

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.

Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies. Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment. Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models. For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829±0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603±0.024 and 0.615±0.075 for RF and SVM classifiers, respectively). We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

15O]H2O PET/MRI for Assessment of Complete Response to Neoadjuvant or Induction Chemotherapy in Patients with Muscle-Invasive Bladder Cancer: A Pilot Study.

Background: Accurate assessment of therapy response to chemotherapy could possibly offer a bladder-sparing approach in selected patients with localized muscle-invasive bladder cancer (MIBC). The aim of this study was to evaluate whether [15O]H2O PET/MRI can be used for assessment of complete local pathological response to preoperative chemotherapy in patients with MIBC. Methods: This prospective pilot study included 13 patients with MIBC treated with neoadjuvant or induction chemotherapy and subsequent radical cystectomy. Patients underwent a [15O]H2O PET/MRI scan before chemotherapy and another scan after chemotherapy before radical cystectomy. Volumes of interest were delineated on T2-weighted MRI and transferred to parametric images for dynamic analysis. Tumor blood flow (TBF) was estimated by [15O]H2O PET. Changes in TBF were compared with histopathology. The Wilcoxon matched-pairs signed-ranks test was used for comparing pre- and post-chemotherapy measurements. Results: Mean TBF decreased by 49%. Mean TBF in complete responders (ypT0N0/ypTis) was not significantly different from non-complete responders (≥ypT1) (p = 0.52). Conclusions: Despite a measurable decrease in TBF after chemotherapy treatment, we were not able to estimate a TBF threshold for identifying complete responders to chemotherapy for MIBC patients. Further studies are needed to elucidate the potential of [15O]H2O PET/MRI in assessing therapy response in MIBC.

The probabilistic performance-based seismic assessment of concrete bridge piers with SMASC reinforcing bars: Effect of pulse-like ground motion and vertical load-to-capacity ratio

Concrete bridge piers are critical components of bridge structures and their performance under seismic loading is of utmost importance. Traditional reinforced concrete bridge piers have shown limitations in terms of residual deformations and seismic resilience. This has led researchers to explore alternative reinforcement materials, such as Shape Memory Alloy (SMA) coupled with steel reinforcing bars, which have demonstrated promising attributes like energy dissipation as well as self-centering capacity. This study aims to fill this gap by evaluating the performance of concrete bridge piers with SMA-Steel coupled (SMASC) reinforcing bars under various intensities of vertical gravity loads and the action of pulse-like ground motion components, throughout a probabilistic framework. To this end, a group of bridge piers with different reinforcement types, including pure steel and SMASC are considered. These piers are subjected to 55 near-fault (NF) pulse-like records as well as 32 far-fault (FF) ground motions, throughout the Incremental Dynamic Analysis (IDA). The influence of distinct frequency components is analyzed by decomposing NF records into low-frequency pulses and high-frequency residual components. Also, the role of pulse to the 1st modal period of the piers (Tp/T1) is investigated by evaluating the piers’ response under the action of NF records, which were clustered into four groups. Results were assessed by evaluating the intensity measure and capacity of the studied piers at the desired performance objectives according to the FHWA manual. Moreover, the mean annual frequencies of exceeding performance limit states and the confidence levels of meeting performance objectives are studied. The results of the study indicate that the dominance of the component of NF ground motions depends on factors such as the intensity of gravity loads, ground motion characteristics, and the Tp/T1 ratio. The components of NF records within certain clusters of the Tp/T1 ratio are necessary for accurate response assessment, while FF records can be used for conservative design purposes, depending on the level of ground motion intensity and the intensity of applied gravity load. The SMASC-reinforced piers with specific λ factors (i.e. λ = 0.5 or λ = 1.0) and low intensity of gravity loads lead to a higher (1.6 times higher) mean annual frequency of exceeding a limit state, compared to pure steel rebars. Also, the confidence levels for meeting performance objectives vary depending on the ground motions, but, as gravity load intensity increases, confidence levels decrease, particularly for piers with a λ factor of 0.5.

Resolving spatial response heterogeneity in glioblastoma.

Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.

Preliminary efficacy and safety of camrelizumab combined with cetuximab and chemotherapy in recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC): A phase II clinical trial.

e18011 Background: Cetuximab and PD-1 inhibitors are widely used first-line therapy for R/M HNSCC and may provide potential synergy in R/M HNSCC. We conducted an open-label, single-arm, Simon's two-stage, phase II study of camrelizumab with cetuximab and cisplatin-based chemotherapy as first-line treatment in R/M HNSCC (NCT05673577). We report the results of stage 1 of this study. Methods: Eligible patients with R/M HNSCC not amenable to curative treatment were enrolled. Patients were treated with camrelizumab 200mg intravenously Q3W, cetuximab 400mg/m2 loading dose followed by 250mg/m2 weekly (21-day cycle), cisplatin 75mg/m2 Q3W, for up to 6 cycles, and nab-paclitaxel 125mg/m2 on d1, d8 (21-day cycle), for up to 6 cycles. Maintenance therapy with camrelizumab 200mg intravenously Q2W, cetuximab 500mg/m2 Q2W were given until intolerable toxicity or disease progression. Simon-minimax design was applied, if 9 of the 21 patients achieve PR or CR in the first stage, another 19 patients will be included. The primary endpoint is ORR. Secondary endpoints include PFS, OS, safety, etc, andmolecular biomarkers will be tested as exploratory endpoints. Results: A total of 21 patients were enrolled in the stage 1, with a median age of 65y (range 34-72y), M:F 19:2,ECOG (0:1) 1:20, and primary sites of oral cavity (n=6), oropharynx (n=3), hypopharynx (n=2), larynx (n=8), and unknown (n=2). By the cutoff date of Jan 10 2024, 20 patients were followed up (1 withdrew because of refusing and discontinuing treatment after SD). In total, 2 (9.5%) CR, 18 (85.7%) PR and 1 (4.8%) SD were observed as best response for an ORR of 95.2% and a DCR of 100%, meeting pre-planned criteria for trial continuation. Meanwhile, 18 patients achieved PR and 1 patient achieved SD after receiving the first two cycles of treatment (2 patients didn't complete accurate radiologic assessment of tumor response after 2 cycles of treatment and were confirmed PR after 4 cycles). Out of 21, 13 patients completed planned cycles of treatment and started maintenance therapy, 5 patients were still under planned cycles. The only one case of progressive disease occurred during the maintenance period. Grade 3 or above treatment-related adverse events were observed in 47.6% patients. One patient discontinued camrelizumab due to pneumonia, and one patient discontinued cetuximab due to grade 3 rash. Three patients reported fatal AEs, which was caused by tumor hemorrhage in the re-irradiated site, severe infectious pneumonia and choking due to swallowing, respectively. Conclusions: Camrelizumab combined with cetuximab and cisplatin-based chemotherapy induced a high ORR with prompt tumor shrinkage and was well tolerated in the scenario of first-line R/M HNSCC. These results meet protocol specifications for trial continuation. Final results will include long-term and stage 2 results. Clinical trial information: NCT05673577 .

Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy.

Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. This retrospective study analyzed 337 ESCC resection specimens from 2020-2021 at the Pudong-Branch (Cohort 1) and 114 from 2021-2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption(Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhancethe efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

Development of a haematological indices-based nomogram for prognostic prediction and immunotherapy response assessment in primary pulmonary lymphoepithelioma-like carcinoma patients.

Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare yet aggressive malignancy. This study aims to investigate a deep learning model based on hematological indices, referred to as haematological indices-based signature (HIBS), and propose multivariable predictive models for accurate prognosis prediction and assessment of therapeutic response to immunotherapy in PPLELC. This retrospective study included 117 patients with PPLELC who received immunotherapy and were randomly divided into a training (n=82) and a validation (n=35) cohort. A total of 41 hematological features were extracted from routine laboratory tests and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to establish the HIBS. Additionally, we developed a nomogram using the HIBS and clinical characteristics through multivariate Cox regression analysis. To evaluate the nomogram's predictive performance, we used calibration curves and calculated the time-dependent area under the curve (AUC). Kaplan-Meier survival analysis was performed to estimate progression-free survival (PFS) in both cohorts. The proposed HIBS comprised 14 hematological features and showed that patients who experienced disease progression had significantly higher HIBS scores compared to those who did not progress (P<0.001). Five prognostic factors, including HIBS, tumor-node-metastasis (TNM) stage, presence of bone metastasis and the specific immunotherapy regimen, were found to be independent factors and were used to construct a nomogram, which effectively categorized PPLELC patients into a high-risk and a low-risk group, with patients in the high-risk patients demonstrating worse PFS (7.0 vs. 18.0 months, P<0.001) and lower overall response rates (22.2% vs. 52.7%, P<0.001). The nomogram showed satisfactory discrimination for PFS, with AUC values of 0.837 and 0.855 in the training and validation cohorts, respectively. The HIBS-based nomogram could effectively predict the PFS and response of patients with PPLELC regarding immunotherapy and serve as a valuable tool for clinical decision making.

Bioinformatics-based analysis of the relationship between disulfidptosis and prognosis and treatment response in pancreatic cancer

Tumor formation is closely associated with disulfidptosis, a new form of cell death induced by disulfide stress-induced. The exact mechanism of action of disulfidptosis in pancreatic cancer (PCa) is not clear. This study analyzed the impact of disulfidptosis-related genes (DRGs) on the prognosis of PCa and identified clusters of DRGs, and based on this, a risk score (RS) signature was developed to assess the impact of RS on the prognosis, immune and chemotherapeutic response of PCa patients. Based on transcriptomic data and clinical information from PCa tissue and normal pancreatic tissue samples obtained from the TCGA and GTEx databases, differentially expressed and differentially surviving DRGs in PCa were identified from among 15 DRGs. Two DRGs clusters were identified by consensus clustering by merging the PCa samples in the GSE183795 dataset. Analysis of DRGs clusters about the PCa tumor microenvironment and differential analysis to obtain differential genes between the two DRG clusters. Patients were then randomized into the training and testing sets, and a prognostic prediction signature associated with disulfidptosis was constructed in the training set. Then all samples were divided into high-disulfidptosis-risk (HDR) and low-disulfidptosis-risk (LDR) subgroups based on the RS calculated from the signature. The predictive efficacy of the signature was assessed by survival analysis, nomograms, correlation analysis of clinicopathological characteristics, and the receiver operating characteristic (ROC) curves. To assess differences between different risk subgroups in immune cell infiltration, expression of immune checkpoint molecules, somatic gene mutations, and effectiveness of immunotherapy and chemotherapy. The GSE57495 dataset was used as external validation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of DRGs. A total of 12 DRGs with differential expression and prognosis in PCa were identified, based on which a risk-prognosis signature containing five differentially expressed genes (DEGs) was developed. The signature was a good predictor and an independent risk factor. The nomogram and calibration curve shows the signature's excellent clinical applicability. Functional enrichment analysis showed that RS was associated with tumor and immune-related pathways. RS was strongly associated with the tumor microenvironment, and analysis of response to immunotherapy and chemotherapy suggests that the signature can be used to assess the sensitivity of treatments. External validation further demonstrated the model's efficacy in predicting the prognosis of PCa patients, with RT-qPCR and immunohistochemical maps visualizing the expression of each gene in PCa cell lines and the tissue. Our study is the first to apply the subtyping model of disulfidptosis to PCa and construct a signature based on the disulfidptosis subtype, which can provide an accurate assessment of prognosis, immunotherapy, and chemotherapy response in PCa patients, providing new targets and directions for the prognosis and treatment of PCa.

NIMG-87. PEDIATRIC BRAIN TUMOR SEGMENTATION INITIATIVE AT CHILDREN’S BRAIN TUMOR NETWORK (CBTN); PROJECT UPDATE ON IMPROVED PERFORMANCE AND FUTURE PROSPECTS

Abstract Pediatric brain tumors (PBT) exhibit significant heterogeneity, and accurate assessment of tumor response is crucial for patient management. While bidimensional measurements of tumor size are commonly used, they may underestimate tumor dimensions. Our group previously developed a multi-institutional, and multi-histology model on the Children’s Brain Tumor Network (CBTN) imaging dataset to segment tumor subregions, recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. In the most recent update, we utilized a self-configuring deep learning architecture called nnU-Net on a large dataset of well-annotated PBTs (233 training, and 60 withheld internal and 46 external test sets). The model was trained and validated on multiparametric MRI sequences and differentiated various tumor subregions, including enhancing tumor (ET), nonenhancing tumor (NET), edema (ED), and cystic component (CC) and the whole tumor (WT) regions. Clinical validity was assessed by comparing the volumes of tumor subregions predicted by the model with expert manual segmentations, demonstrating strong agreement, with statistically significant Pearson’s correlation coefficients of 0.93/0.69 for ET, 0.94/0.93 for NET, 0.78/0.93 for CC, and 0.94/0.50 for ED subregions (p&amp;lt; 0.001) for the internal/external test cohorts. The trained model showed excellent performance for the withheld internal test set and decent accuracy for the external test set. Median Dice scores for internal/external test sets were 0.94±0.10/0.90±0.07 for WT, 0.85±0.33/0.84±0.30 for ET, 0.80±0.32/0.64±0.31 for NET, 0.79±0.37/0.67±0.33 for CC, 0.70 ±0.42/0.37±0.43 for ED, and 0.86±0.19/0.80±0.21 for all nonenhancing components (combination of NET, CC, and ED), respectively. The proposed automated segmentation method provides accurate and reproducible volumetric measurements of RAPNO-defined subregions in PBTs. It exhibits robust performance and potential generalization to external datasets and the accuracy is higher compared to the previous DeepMedic model. We will continue to improve the model by constantly adding new training data to further improve the performance and sustain model robustness.

Early Assessment of Radiation Treatment at Clinical Field Strength by D2O Administration and Deuterium MRI

An accurate and non-invasive assessment of tumor response following treatment is essential. Traditional anatomical imaging techniques are insufficient before a significant morphological change can be observed. Metabolic imaging of molecular processes in the living body is also used. In recent years, deuterium magnetic resonance spectroscopic (MRS) imaging has been demonstrated as an alternative for cancer metabolic imaging by high-field (4-11T) MRI using deuterium-labeled molecules as a contrast agent. The study aim was to evaluate the feasibility of using deuterium MRI 1.5T for tumor visualization and early assessment of the efficacy of three anticancer treatment strategies (radiation and anticancer drugs) in pancreatic cancer model mice given heavy water (D2O) to induce deuterium (2H)-tissue labeling. A MIA PaCa-2 pancreatic cancer model of six BALB/c-nu mice was prepared, and repeated deuterium MRI was performed during the first 10 days after starting free drinking of 30% D2O. We also evaluated 2H accumulation in the tumor after irradiation, bevacizumab administration, or gemcitabine administration of other 20 mice. Additional confirmatory proton MRI, ex vivo metabolic hyperpolarization 13C-MRS and histopathology were performed. The mouse's whole-body distribution of 2H was visible 1 day after drinking, and the signal intensity increased daily. Although the tumor size did not change 1 and 3 days after irradiation, the amount of 2H in the tumor decreased significantly. The 2H image intensity of the tumor also significantly decreased after the administration of bevacizumab or gemcitabine. Metabolic hyperpolarization 13C-MRS, proton MRI and 2H-NMR spectroscopy confirmed the efficacy of the anticancer treatments. Deuterium MRI at 1.5T proved feasibility to track 2H distribution throughout mouse tissues during D2O administration and revealed a higher 2H accumulation in the tumor xenografts. This research demonstrated a promising successful method for early assessment of radiotherapy and chemotherapy of pancreatic cancer.

Deuterium Magnetic Resonance Imaging Using Deuterated Water-Induced 2H-Tissue Labeling Allows Monitoring Cancer Treatment at Clinical Field Strength.

An accurate and noninvasive assessment of tumor response following treatment other than traditional anatomical imaging techniques is essential. Deuterium magnetic resonance spectroscopic (MRS) imaging has been demonstrated as an alternative for cancer metabolic imaging by high-field MRI using deuterium-labeled molecules. The study aim was to use 2H tissue labeling and deuterium MRI at clinical field strength for tumor visualization and assessment of three anticancer therapies in pancreatic cancer model mice. MIA PaCa-2 pancreatic carcinoma and C26 colorectal carcinoma models of BALB/c-nu mice was prepared, and repeated deuterium MRI was performed during the first 10 days of free drinking of 30% D2O to track 2H distribution in tissues. 2H accumulation in the tumor after irradiation, bevacizumab administration, or gemcitabine administration was also measured in MIA PaCa-2-bearing mice. Confirmatory proton MRI, ex vivo metabolic hyperpolarization 13C-MRS, and histopathology were performed. The mouse's whole-body distribution of 2H was visible 1 day after drinking, and the signal intensity increased daily. Although the tumor size did not change 1 and 3 days after irradiation, the amount of 2H decreased significantly. The 2H image intensity of the tumor also significantly decreased after the administration of bevacizumab or gemcitabine. Metabolic hyperpolarization 13C-MRS, proton MRI, and 2H-NMR spectroscopy confirmed the efficacy of the anticancer treatments. Deuterium MRI at 1.5T proved feasible to track 2H distribution throughout mouse tissues during D2O administration and revealed a higher 2H accumulation in the tumor xenografts. This research demonstrated a promising successful method for preliminary assessment of radiotherapy and chemotherapy of cancer.

Giant adrenal metastasis of malignant melanoma: impact of 18F-FDG PET/CT in early assessment of dramatic response to immunotherapy: case report

BackgroundThis case represents one of the largest adrenal metastases in the literature and highlights the importance of accurate imaging in clinical management of patients with advanced malignant melanoma.Case presentationA 67-year-old female with an ulcerated malignant melanoma presented with an asymptomatic 18-cm left adrenal mass on 18F-FDG PET/CT staging. After two cycles of combined systemic immunotherapy, an impressive shrinkage of the adrenal metastatic tumor was observed on 18F-FDG PET/CT imaging.ConclusionsThe case emphasizes the potential of 18F-FDG PET/CT as a functional imaging modality for accurate staging and precise early assessment of response to immunotherapy. Overall, this report highlights the importance of accurate imaging for the effective management of adrenal metastases in melanoma patients.

Radiomic Features Are Predictive of Response in Rectal Cancer Undergoing Therapy.

Rectal cancer is a major mortality cause in the United States (US), and its treatment is based on individual risk factors for recurrence in each patient. In patients with rectal cancer, accurate assessment of response to chemoradiotherapy has increased in importance as the variety of treatment options has grown. In this scenario, a controversial non-operative approach may be considered in some patients for whom complete tumor regression is believed to have occurred. The recommended treatment for locally advanced rectal cancer (LARC, T3-4 ± N+) is total mesorectal excision (TME) after neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has become a standard technique for local staging of rectal cancer (tumor, lymph node, and circumferential resection margin [CRM] staging), in both the US and Europe, and it is getting widely used for restaging purposes. In our study, we aimed to use an MRI radiomic model to identify features linked to the different responses of chemoradiotherapy of rectal cancer before surgery, and whether these features are helpful to understand the effectiveness of the treatments. We retrospectively evaluated adult patients diagnosed with LARC who were subjected to at least 2 MRI examinations in 10-12 weeks at our hospital, before and after nCRT. The MRI acquisition protocol for the 2 exams included T2 sequence and apparent diffusion coefficient (ADC) map. The patients were divided into 2 groups according to the treatment response: complete or good responders (Group 1) and incomplete or poor responders (Group 2). MRI images were segmented, and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model. We included 38 patients (26 males and 12 females), who are classified from T2 and T4 stages in the rectal cancer TNM. After the nCRT, the patients were divided into Group 1 (13 patients), complete or good responders, and Group 2 (25 patients), incomplete or poor responders. Analysis at baseline generated the following significant features for the Mann-Whitney test (out of a total of 107) for each sequence. Also, the analysis at the end of the follow-up yielded a high number of significant features for the Mann-Whitney test (out of a total of 107) for each image. Features selected by the LASSO regression method for each image analyzed; ROC curves relative to each model are represented. We developed an MRI-based radiomic model that is able to differentiate and predict between responders and non-responders who went through nCRT for rectal cancer. This approach might identify early lesions with high surgical potential from lesions potentially resolving after medical treatment.

Response Assessment of Treated Hepatocellular Carcinoma

AbstractHepatocellular carcinoma (HCC) is a leading cause of morbidity and mortality worldwide, including in India. The incidence of HCC has been rising due to lifestyle diseases such as obesity, diabetes, non-alcoholic fatty liver disease (NAFLD), and alcoholic liver disease (ALD), as well as viral hepatitis infections. Various locoregional therapies (LRTs) are used to treat HCC, including thermal ablation, transarterial therapies, stereotactic body radiotherapy (SBRT), and transarterial radioembolization (TARE). Traditional response evaluation criteria like WHO and RECIST, which rely on size-based measurements, may not accurately assess treatment response to LRTs. To address this limitation, modified response evaluation criteria for solid tumors (mRECIST) and the LI-RADS treatment response algorithm (LR-TRA) have been developed. mRECIST assesses patient-level response, while LR-TRA provides lesion-level response assessment specifically for HCC treated with LRTs. This article discusses the imaging protocols for diagnosing HCC and the imaging appearances of treated lesions after different LRTs. It explains the criteria for categorizing treatment response, such as LR-TR viable, LR-TR non-viable, and LR-TR equivocal. It also highlights the challenges and future directions in response assessment, including the incorporation of ancillary findings, the assessment of patients receiving a combination of locoregional and systemic therapies, and the potential use of biomarkers like serum AFP, AFP-L3, and PIVKA-II. In conclusion, locoregional therapies have expanded the treatment options for HCC, and accurate response assessment is crucial for optimizing patient management. mRECIST and LR-TRA provide valuable tools for evaluating treatment response, and future updates are expected to address specific challenges and incorporate newer approaches like iRECIST and quantitative imaging assessment. Additionally, the use of biomarkers may complement imaging-based response assessment in the future.

Soft Tissue Ewing Sarcoma Cell Drug Resistance Revisited: A Systems Biology Approach.

Ewing sarcoma is a rare type of cancer that develops in the bones and soft tissues. Drug therapy represents an extensively used modality for the treatment of sarcomas. However, cancer cells tend to develop resistance to antineoplastic agents, thereby posing a major barrier in treatment effectiveness. Thus, there is a need to uncover the molecular mechanisms underlying chemoresistance in sarcomas and, hence, to enhance the anticancer treatment outcome. In this study, a differential gene expression analysis was conducted on high-throughput transcriptomic data of chemoresistant versus chemoresponsive Ewing sarcoma cells. By applying functional enrichment analysis and protein-protein interactions on the differentially expressed genes and their corresponding products, we uncovered genes with a hub role in drug resistance. Granted that non-coding RNA epigenetic regulators play a pivotal role in chemotherapy by targeting genes associated with drug response, we investigated the non-coding RNA molecules that potentially regulate the expression of the detected chemoresistance genes. Of particular importance, some chemoresistance-relevant genes were associated with the autonomic nervous system, suggesting the involvement of the latter in the drug response. The findings of this study could be taken into consideration in the clinical setting for the accurate assessment of drug response in sarcoma patients and the application of tailored therapeutic strategies.