Predict Tumor Response Research Articles

Dissecting the Impact of Genetic Background on Oncogenic Response to Radiation Exposure in the Ptch1+/- Mouse Model.

Medulloblastoma (MB) is a common primary brain cancer in children. The sonic hedgehog (SHH) pathway is indispensable for the normal development of the cerebellum, and MB is often caused by persistent SHH activation owing to mutations in pathway components. Patched1 (PTCH1) is the primary receptor for the SHH ligand and a negative regulator of the SHH signal transduction pathway. Mice heterozygous for the Ptch1 gene (Ptch1+/-) are predisposed to MB development. Irradiation of newborn Ptch1+/- mice dramatically increases MB occurrence. A genetic background carrying the Ptch1 mutation significantly influences the risk of developing MB. This study aims to investigate the genetic background-related mechanisms that regulate radiation-induced cellular response and oncogenesis in the cerebellum. We employed multiple approaches, including: (a) analysis of cellular radiosensitivity in granule cell precursors (GCPs), the MB cells of origin, derived from Ptch1 mice with a genetic background that is sensitive (CD1) or resistant (C57Bl/6) to the induction of radiogenic MB; (b) identification of genes differentially expressed in spontaneous and radiation-induced MBs from these two mouse strains; (c) bioinformatic analysis to correlate the expression of radiation-induced genes with survival in MB patients; and (d) examining the expression of these genes in ex vivo MBs induced by single or repeated radiation doses. We have identified a potential gene expression signature-Trp53bp1, Bax, Cyclin D1, p21, and Nanog-that influences tumor response. In ex vivo cultured spontaneous MBs, the expression levels of these genes increase after irradiation in CD1 mice, but not in mice with a C57Bl/6 genetic background, suggesting that this signature could predict tumor response to radiation therapy and help develop strategies for targeting DNA damage repair in tumors. A detailed understanding of the mechanisms behind genetic background-related susceptibility to radiation-induced oncogenic responses is crucial for translational research.

Prediction of tumor response to neoadjuvant chemotherapy in high-grade osteosarcoma using clustering-based analysis of magnetic resonance imaging: an exploratory study.

To evaluate the ability of magnetic resonance imaging (MRI)-based clustering analysis to predict the pathological response to neoadjuvant chemotherapy (NACT) in patients with primary high-grade osteosarcoma. Twenty-two patients were included in this retrospective study. All patients underwent MRIs before and after NACT. The entire tumor volume was manually delineated on post-contrast T1-weighted images and subsegmented into three clusters using the K-means algorithm. Histogram-based parameters were calculated for each lesion. The response to NACT was obtained from the histopathological assessment of the tumor necrosis rate following resection. The Mann-Whitney test was used to compare poor and fair-to-good responders. The receiver operating characteristic curve was used to evaluate the diagnostic performance of the optimal parameters. At baseline, poor responders showed a significantly larger volume of cluster1 (Vol1) than fair-to-good responders (p = 0.038). After NACT, they exhibited a lower 10th percentile (P10) and kurtosis (p = 0.038 and 0.002, respectively). Vol1 at baseline and P10 after NACT had an AUC of 77% (95% CI 56-98%). The kurtosis after NACT had the best discriminative power, with an AUC of 89.7% (95% CI 75-100%). The MRI-based histogram and clustering analysis provided a good ability to differentiate between poor and fair-to-good responders before and after NACT. Further investigations using larger datasets are required to corroborate our findings.

Dynamics of cell-free tumor DNA correlate with early MRI response during chemoradiotherapy in rectal cancer

BackgroundIn locally advanced rectal cancer, the prediction of tumor response during and after neoadjuvant treatment remains challenging. In terms of organ preservation, adaptive radiotherapy, and intensified (total) neoadjuvant therapies, biomarkers are desirable for patient stratification.MethodsIn 16 patients, weekly blood samples (n = 86) to detect cell-free tumor DNA (ctDNA) during long-course neoadjuvant chemoradiotherapy were analyzed. Data were correlated with initial tumor volumes, MRI response in week 2 and 5 of radiotherapy as well as with pathologic tumor response after resection and outcome parameters.ResultsMost patients showed decreasing ctDNA during the course of radiochemotherapy. However, we found heterogenous dynamics of ctDNA and could identify three groups: (1) decline (2) no clear decline and/or late shedding (3) persistence of ctDNA. In seven patients we could detect significant amounts of ctDNA in week 5 or week 6 of treatment. In our pilot cohort, we did not find significant correlations of ctDNA dynamics with pathologic response or outcome parameters. However, patients with distinct decline of ctDNA had larger tumor volumes prior to treatment, and MRI imaging in week 2 and 5 revealed bigger absolute decrease of tumor volumes. If significant levels of ctDNA were found in week 5 and / or 6, patients showed less absolute tumor volume decrease in week 2 and 5.ConclusionsWeekly measurement of ctDNA during radiochemotherapy is feasible and might represent a promising biomarker. Bigger initial primary tumors showed different ctDNA shedding profiles compared with smaller primary tumors and correlations of ctDNA dynamics with early imaging response were found.

Diversity of the Circulating Tumor Markers: Perspectives of a Multimodal Liquid Biopsy.

Over the past decade, liquid biopsy(LB) has become a routine diagnostic test essential for the treatment of malignant tumors of various localizations. Its capabilities include early diagnosis, molecular genotyping, prognosis, prediction, and monitoring of tumor response. Typically, liquid biopsy involves the extraction of a single type of tumor-derived molecules or cellular elements from blood and subsequent molecular analysis. These elements may include circulating tumor DNA(ctDNA), circulating tumor cells(CTCs), circulating tumor RNA(ctRNA), or contents of extracellular vesicles (exosomes). Despite the technical sophistication of molecular analysis methods for circulating biomarkers, this diagnostic approach has limited relevance. In a significant proportion of cancer patients (ranging from 10 to 50%, depending on the tumor type), none of these analytes can be detected and analyzed, even in the presence of large, progressing neoplastic foci in the body. It seems reasonable to suggest that heterogeneous fractions of the circulating tumor-specific biomarkers complement each other, thus simultaneous analysis of several fractions will not only increase sensitivity of the method but also more accurately characterize and predict the clinical situation. This review examines the possibilities and advantages of applying a combined multiparametric approach to liquid biopsy, which involves testing multiple circulating analytes in a single blood sample.

Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer

AimThis study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes. Materials and methodsNinety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (β), spatial parameter (μ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX-median) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance. ResultsPre-treatmentβ-10th percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Δβ-median showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %). DiscussionThe use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.

A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models

BackgroundIn the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied “desmoplastic” tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics.MethodsWe developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable, and non-responsive tumors to chemotherapy, immunotherapy, or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized.ResultsWe present strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes.ConclusionsThis approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes.

The value of predicting neoadjuvant chemotherapy early efficacy in nasopharyngeal carcinoma based on amide proton transfer imaging and diffusion weighted imaging.

Early detection of nasopharyngeal carcinoma (NPC) patients who are not sensitive to neoadjuvant chemotherapy (NAC) can guard against overtreatment. This study aimed to evaluate the effectiveness of amide proton transfer (APT) imaging and diffusion-weighted imaging (DWI) in predicting the early response to NAC in patients with NPC. This prospective study enrolled fifty patients with biopsy-confirmed NPC from September 2021 to May 2023. Magnetic resonance imaging (MRI) including APT and DWI, was performed before NAC. After NAC, patients were divided into complete response (CR), partial response (PR), and stable disease (SD) and progressive disease (PD) groups based on the Response Evaluation Criteria in Solid Tumours Version 1.1. The Kruskal-Wallis H test was used for statistical analysis. The differences in APT and apparent diffusion coefficient (ADC) values among the different efficacy groups were compared, the receiver operating characteristic (ROC) curve was drawn for statistically significant parameters, and the area under the curve (AUC) was calculated. Fifty patients (mean age: 47±14 years; 42 males and 8 females) were included in the final analysis (11 were in the CR group, 30 in the PR group, 9 in the SD group, and 0 in the PD group). The ADC values showed no significant differences among the different treatment response groups. The SD group showed significantly lower APTmax (P=0.025), APTskewness (P=0.025) and APT90% (P=0.001) values than the CR and PR groups. Setting APT90% =3.10% as the cut-off value, optimal diagnostic performance (AUC: 0.831; sensitivity: 0.778; specificity: 0.878) was obtained in predicting the SD group. APT imaging can predict the early tumour response to NAC in patients with NPC. APT imaging may be superior to DWI in predicting tumour response.

Tumor Growth Pattern and Intra- and Peritumoral Radiomics Combined for Prediction of Initial TACE Outcome in Patients with Primary Hepatocellular Carcinoma.

Non-invasive methods are urgently needed to assess the efficacy of transarterial chemoembolization (TACE) and to identify patients with hepatocellular carcinoma (HCC) who may benefit from this procedure. This study, therefore, aimed to investigate the predictive ability of tumor growth patterns and radiomics features from contrast-enhanced magnetic resonance imaging (CE-MRI) in predicting tumor response to TACE among patients with HCC. A retrospective study was conducted on 133 patients with HCC who underwent TACE at three centers between January 2015 and April 2023. Enrolled patients were divided into training, testing, and validation cohorts. Rim arterial phase hyperenhancement (Rim APHE), tumor growth patterns, nonperipheral washout, markedly low apparent diffusion coefficient (ADC) value, intratumoral arteries, and clinical baseline features were documented for all patients. Radiomics features were extracted from the intratumoral and peritumoral regions across the three phases of CE-MRI. Seven prediction models were developed, and their performances were evaluated using receiver operating characteristic (ROC) and decision curve analysis (DCA). Tumor growth patterns and albumin-bilirubin (ALBI) score were significantly correlated with tumor response. Tumor growth patterns also showed a positive correlation with tumor burden (r = 0.634, P = 0.000). The Peritumor (AUC = 0.85, 0.71, and 0.77), Clinics_Peritumor (AUC = 0.86, 0.77, and 0.81), and Tumor_Peritumor (AUC = 0.87, 0.77, and 0.80) models significantly outperformed the Clinics and Tumor models (P < 0.05), while the Clinics_Tumor_Peritumor model (AUC = 0.88, 0.81, and 0.81) outperformed the Clinics (AUC = 0.67, 0.77, and 0.75), Tumor (AUC = 0.78, 0.72, and 0.68), and Clinics_Tumor (AUC = 0.82, 0.83, and 0.78) models (P < 0.05 or 0.053, respectively). The DCA curve demonstrated better predictive performance within a specific threshold probability range for Clinics_Tumor_Peritumor. Combining tumor growth patterns, intra- and peri-tumoral radiomics features, and ALBI score could be a robust tool for non-invasive and personalized prediction of treatment response to TACE in patients with HCC.

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

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

Immune Indicator Changes in Hepatocellular Carcinoma Undergoing TACE Plus ICIs and Anti-VEGF Antibodies/TKIs: A Prognostic Biomarker Analysis.

To explore changing trends in circulating immune indicators of hepatocellular carcinoma (HCC) undergoing TACE plus immune checkpoint inhibitors (ICIs) and anti-VEGF antibodies/TKIs and to elucidate the relationship between immune response and tumor prognosis. This single-center retrospective study included patients with unresectable HCC undergoing TACE plus ICIs and anti-VEGF antibodies/TKIs from March 11, 2019, to February 15, 2024. Peripheral blood samples were collected at baseline and every cycle, from which blood cell counts and immune indicators were analyzed. The primary outcome was the objective response rate (ORR) at the first evaluation. According to the first evaluation based on mRECIST, patients were classified into PD, SD, and OR groups for analysis. Further subgroup analysis was performed on the OR group based on whether experiencing progression after the first evaluation. Lymphocyte subsets were measured by flow cytometry. Immunoglobulins were measured using the immune turbidimetric method. The neutrophil-to-lymphocyte ratio (NLR) was measured by the complete blood count. Simple linear regression was employed to examine the dynamic trends. A total of 63 patients were enrolled, with an ORR of 55.6% and a disease control rate (DCR) of 87.3% at the first evaluation. The median overall survival (mOS) was 27.5 months (95% CI: 22.5-32.5 months). In the OR group (n=35), more active immune responses, expressed in a decrease in CD3-CD19+ (p=0.004), CFB (p=0.027), NLR (p<0.001) and an increase in Ig λ (p=0.010), Ig κ (p=0.037), Ig A (p=0.005), Ig G (p=0.006), were related to better prognosis, while similar patterns seen in the OR-nPD subgroup. Concurrently, no significant differences were noted in the PD group (n=8). The combination therapy may modify the tumor microenvironment of HCC. Changing trends in circulating immune indicators and NLR can serve as potential biomarkers for predicting tumor response and guiding clinical treatment.

Radiomics in Predicting Tumour Response to Radiation Therapy in Lung Cancer: A multi-institutional Study

Radiomics in Predicting Tumour Response to Radiation Therapy in Lung Cancer: A multi-institutional Study

Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors.

Radiomics models have been widely exploited in oncology for the investigation of tumor classification, as well as for predicting tumor response to treatment and genomic sequence; however, their performance in veterinary gastrointestinal tumors remains unexplored. Here, we sought to investigate and compare the performance of radiomics models in various settings for differentiating among canine small intestinal adenocarcinoma, lymphoma, and spindle cell sarcoma. Forty-two small intestinal tumors were contoured using four different segmentation methods: pre- or post-contrast, each with or without the inclusion of intraluminal gas. The mesenteric lymph nodes of pre- and post-contrast images were also contoured. The bin settings included bin count and bin width of 16, 32, 64, 128, and 256. Multinomial logistic regression, random forest, and support vector machine models were used to construct radiomics models. Using features from both primary tumors and lymph nodes showed significantly better performance than modeling using only the radiomics features of primary tumors, which indicated that the inclusion of mesenteric lymph nodes aids model performance. The support vector machine model exhibited significantly superior performance compared with the multinomial logistic regression and random forest models. Combining radiologic findings with radiomics features improved performance compared to using only radiomics features, highlighting the importance of radiologic findings in model building. A support vector machine model consisting of radiologic findings, primary tumors, and lymph node radiomics features with bin count 16 in post-contrast images with the exclusion of intraluminal gas showed the best performance among the various models tested. In conclusion, this study suggests that mesenteric lymph node segmentation and radiological findings should be integrated to build a potent radiomics model capable of differentiating among small intestinal tumors.

Predictive value of mono-exponential and multiple mathematical models in locally advanced rectal cancer response to neoadjuvant chemoradiotherapy.

This prospective study aimed to assess the predictive value of mono-exponential and multiple mathematical diffusion-weighted imaging (DWI) models in determining the response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). The study included 103 LARC patients scheduled for preoperative chemoradiotherapy between December 2021 and June 2023 Magnetic resonance imaging (MRI) scans were performed using a 3.0-T MR scanner, encompassing sagittal, axial, and oblique coronal T2-weighted images without fat saturation, along with DWI perpendicular to the rectum's long axis. Various DWI parameters, including apparent diffusion coefficient (ADC), stretched exponential model (SEM), continuous-time random-walk model (CTRW), and fractional-order calculus model (FROC), were measured. The pathologic complete response (pCR) rate and tumor downstaging (T-downstage) rate were determined. After nCRT, SEM-α, SEM-DDC, CTRW-α, CTRW-β, CTRW-D, FROC-β, and ADC values were significantly higher in the pCR group compared to the non-pCR group (all P < 0.05). SEM-DDC, CTRW-α, CTRW-D, FROC-β, FROC-µ, and ADC values were significantly higher in the T-downstage group (ypT0-1) than in the non-T-downstage group (ypT2-4) (P < 0.05). The combination of CTRW (α + β + D) exhibited the best diagnostic performance for assessing pCR after nCRT (AUC = 0.840, P < 0.001). Pre-nCRT CTRW (α + β) demonstrated a predictive AUC of 0.652 (95%CI: 0.552-0.743), 90.3% sensitivity, and 43.1% specificity for pCR. Regarding T-downstage assessment after nCRT, the combination of CTRW (α + D) yielded the best diagnostic performance (AUC = 0.877, P = 0.048). In LARC patients, imaging markers derived from CTRW show promise in predicting tumor response before nCRT and assessing pCR after nCRT.

The Usefulness of Serum Interleukin-6 as a Predictor of Response to Atezolizumab plus Bevacizumab Combination Treatment in Hepatocellular Carcinoma

Introduction: In atezolizumab plus bevacizumab (Atezo/Bev) combination treatment, both drugs act on the immune system. Previously, we reported that immunological changes after Atezo/Bev administration for unresectable hepatocellular carcinoma (uHCC) revealed significant alterations in interleukin (IL)-6, soluble IL-2 receptor, tumor necrosis factor-alpha, and programmed cell death-1 levels. Among these variable factors, serum levels of IL-6 can be easily measured on a commercial basis. Therefore, this study aimed to investigate the utility of serum IL-6 as a predictor of tumor response to Atezo/Bev treatment for uHCC. Method: The study included 44 patients with HCC treated with Atezo/Bev. Blood samples were collected before and 3 weeks after treatment, and tumor response was assessed using contrast-enhanced computed tomography 6 weeks after treatment. Results: Significant changes in serum IL-6 levels were observed in patients treated with Atezo/Bev as first-line therapy but not in those treated with it as second-line or later-line therapy. In patients treated with Atezo/Bev as first-line therapy, serum IL-6 levels increased significantly after treatment in patients with a complete or partial response but not in patients with stable or progressive disease. Furthermore, compared to other tumor markers such as alpha-fetoprotein, Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein, and des-gamma-carboxyprothrombin, serum IL-6 levels exhibited the highest sensitivity in predicting tumor response during the treatment period. Conclusion: In patients with uHCC treated with Atezo/Bev, serum IL-6 levels could serve as a potential predictor of tumor response. Elevated levels after treatment may indicate a favorable tumor response and prognosis.

Correlation between exosomal PD-L1 and prognosis in patients with cancer: a systematic review and meta-analysis.

The predictive role of exosomal programmed cell death ligand l (exoPD-L1) in prognosis has been studied extensively; however, there is still no consensus. Three databases, including EMBASE, PubMed, and Web of Science, were searched through January 4, 2024. The pooled hazard ratios (HRs) with 95% confidence intervals (95%CIs) were used to identify the relationship between circulating exoPD-L1 and prognosis. 15 studies with 1091 patients with cancer were included in this statistical analysis. High exoPD-L1 level was correlated with shorter progression-free survival (PFS) (HR = 2.58, 95% CI: 1.75-3.81) and overall survival (OS) (HR = 1.61, 95% CI: 1.32-1.98). Meanwhile, we found that dynamic upregulation of circulating exoPD-L1 in the early stages of immunotherapy was a favorable factor for prognosis (PFS: HR = 0.34, 95% CI: 0.23-0.51; OS: HR = 0.21, 95% CI: 0.13-0.26). Circulating exoPD-L1 may be a valuable prognostic indicator for patients with cancer and monitoring its changes in the early stages of immunotherapy might be used to predict tumor response and clinical outcome. This conclusion may not apply to superficial tumors.

Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition

Constructing digital twins for predictive tumor treatment response models can have a high computational demand that presents a practical barrier for their clinical adoption. In this work, we demonstrate that proper orthogonal decomposition, by which a low-dimensional representation of the full model is constructed, can be used to dramatically reduce the computational time required to calibrate a partial differential equation model to magnetic resonance imaging (MRI) data for rapid predictions of tumor growth and response to chemotherapy. In the proposed formulation, the reduction basis is based on each patient’s own MRI data and controls the overall size of the “reduced order model”. Using the full model as the reference, we validate that the reduced order mathematical model can accurately predict response in 50 triple negative breast cancer patients receiving standard of care neoadjuvant chemotherapy. The concordance correlation coefficient between the full and reduced order models was 0.986 ± 0.012 (mean ± standard deviation) for predicting changes in both tumor volume and cellularity across the entire model family, with a corresponding median local error (inter-quartile range) of 4.36 % (1.22 %, 15.04 %). The total time to estimate parameters and to predict response dramatically improves with the reduced framework. Specifically, the reduced order model accelerates our calibration by a factor of (mean ± standard deviation) 378.4 ± 279.8 when compared to the full order model for a non-mechanically coupled model. This enormous reduction in computational time can directly help realize the practical construction of digital twins when the access to computational resources is limited.

Tumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making.

To compare the performance of 1D and 3D tumor response assessment for predicting median overall survival (mOS) in patients who underwent immunotherapy for hepatocellular carcinoma (HCC). Patients with HCC who underwent immunotherapy between 2017 and 2023 and received multi-phasic contrast-enhanced MRIs pre- and post-treatment were included in this retrospective study. Tumor response was measured using 1D, RECIST 1.1, and mRECIST, and 3D, volumetric, and percentage quantitative EASL (vqEASL and %qEASL). Patients were grouped into disease control vs progression and responders vs non-responders. Kaplan-Meier curves analyzed with log-rank tests assessed the predictive value for mOS. Cox regression modeling evaluated the association of clinical baseline parameters with mOS. This study included 37 patients (mean age, 69.1 years [SD, 8.0]; 33 men). The mOS was 16.9 months. 3D vqEASL and %qEASL successfully stratified patients into disease control and progression (vqEASL: HR 0.21, CI: 0.55-0.08, p < 0.001; %qEASL: HR 0.18, CI: 0.83-0.04, p = 0.013), as well as responder and nonresponder (vqEASL: HR 0.25, CI: 0.08-0.74, p = 0.007; %qEASL: HR 0.17, CI: 0.04-0.72, p = 0.007) for predicting mOS. The 1D criteria, mRECIST stratified into disease control and progression only (HR 0.24, CI: 0.65-0.09, p = 0.002), and RECIST 1.1 showed no predictive value in either stratification. Multivariate Cox regression identified alpha-fetoprotein > 500 ng/mL as a predictor for poor mOS (p = 0.04). The 3D quantitative enhancement-based response assessment tool qEASL can predict overall survival in patients undergoing immunotherapy for HCC and could identify non-responders. Using 3D quantitative enhancement-based tumor response criteria (qEASL), radiologists' predictions of tumor response in patients undergoing immunotherapy for HCC can be further improved. MRI-based tumor response criteria predict immunotherapy survival benefits in HCC patients. 3D tumor response assessment methods surpass current evaluation criteria in predicting overall survival during HCC immunotherapy. Enhancement-based 3D tumor response criteria are robust prognosticators of survival for HCC patients on immunotherapy.

Utilizing a Pathomics Biomarker to Predict the Effectiveness of Bevacizumab in Ovarian Cancer Treatment.

The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model's confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage.

Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases.

The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy. We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses. Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605-0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8-22.2), and 14.8 months (95% CI, 14.2-15.4), respectively (P = 0.431, log-rank test). Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.

Characterization of an expanded set of assays for immunomodulatory proteins using targeted mass spectrometry

Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer.