Early Prediction Of Treatment Response Research Articles

Quantitative ultrasound radiomics guided adaptive neoadjuvant chemotherapy in breast cancer: early results from a randomized feasibility study.

In patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC), quantitative ultrasound (QUS) radiomics can predict final responses early within 4 of 16-18 weeks of treatment. The current study was planned to study the feasibility of a QUS-radiomics model-guided adaptive chemotherapy. The phase 2 open-label randomized controlled trial included patients with LABC planned for NAC. Patients were randomly allocated in 1:1 ratio to a standard arm or experimental arm stratified by hormonal receptor status. All patients were planned for standard anthracycline and taxane-based NAC as decided by their medical oncologist. Patients underwent QUS imaging using a clinical ultrasound device before the initiation of NAC and after the 1st and 4th weeks of treatment. A support vector machine-based radiomics model developed from an earlier cohort of patients was used to predict treatment response at the 4th week of NAC. In the standard arm, patients continued to receive planned chemotherapy with the treating oncologists blinded to results. In the experimental arm, the QUS-based prediction was conveyed to the responsible oncologist, and any changes to the planned chemotherapy for predicted non-responders were made by the responsible oncologist. All patients underwent surgery following NAC, and the final response was evaluated based on histopathological examination. Between June 2018 and July 2021, 60 patients were accrued in the study arm, with 28 patients in each arm available for final analysis. In patients without a change in chemotherapy regimen (53 of 56 patients total), the QUS-radiomics model at week 4 of NAC that was used demonstrated an accuracy of 97%, respectively, in predicting the final treatment response. Seven patients were predicted to be non-responders (observational arm (n=2), experimental arm (n=5)). Three of 5 non-responders in the experimental arm had chemotherapy regimens adapted with an early initiation of taxane therapy or chemotherapy intensification, or early surgery and ended up as responders on final evaluation. The study demonstrates the feasibility of QUS-radiomics adapted guided NAC for patients with breast cancer. The ability of a QUS-based model in the early prediction of treatment response was prospectively validated in the current study. clinicaltrials.gov, ID NCT04050228.

Early Prediction of Treatment Response By Circulating Tumor DNA Profiling in Patients with Diffuse Large B-Cell Lymphoma Receiving CAR T-Cell Therapy

Introduction Patients with relapsed or refractory diffuse large B-cell lymphoma (r/r DLBCL) are characterized by a particularly poor prognosis. Anti-CD19 chimeric antigen receptor (CAR) T-cell therapy has improved outcomes of patients with r/r DLBCL, yet a substantial proportion of patients still experiences relapse or progression after CAR T-cell treatment. Identifying patients at high risk of CAR T-cell therapy resistance or future lymphoma progression remains challenging. Here, we explored the value of circulating tumor DNA (ctDNA) as a prognostic biomarker at baseline and early into treatment in DLBCL patients undergoing standard-of-care CAR T-cell therapy, without the need for matched tumor genotypes. We further investigated associations between ctDNA and other known clinical risk factors. Methods We applied targeted next-generation sequencing (NGS) to a total of 53 samples from r/r DLBCL patients receiving CAR T-cell therapy (axicabtagene ciloleucel and tisagenlecleucel, n=16) at the University Medical Center Freiburg (Germany), using a modified version of the AVENIO ctDNA analysis workflow (Roche; Research Use Only) that covered ~314kb and targeted 466 distinct genes recurrently mutated in DLBCL (based on the CAPP-Seq workflow, Kurtz DM et al. J Clin Oncol 2018). Tumor genotypes were assessed noninvasively from plasma samples obtained at baseline before lymphodepletion with matched germline controls. Concentrations of ctDNA were quantified at baseline and early after CAR T-cell therapy at day 7-10. Initial treatment response was assessed by conventional PET-CT obtained 4-6 weeks after treatment based on the Lugano criteria. Results At baseline prior to lymphodepletion, we detected somatic mutations in 100% of plasma samples by noninvasive genotyping, with a median of 19.5 mutations per patient (range: 3-97). The most frequently mutated genes were IGHV, IGLL5, BCL2, MYC, BTG1, CREBBP, and TP53. Patients with high baseline ctDNA concentrations (mean allele frequency [AF] >= 1%) showed shorter progression-free survivial (PFS) and overall survival (OS) than patients with ctDNA levels below this threshold (median PFS: 86.5 days vs. not reached, p=0.04, HR: 6.3, 95% CI: 1.7-23.6; median OS: 219 days vs. not reached, p=0.048, HR: 4.1, 95% CI 1.0-16.6, Figure 1). Pretreatment ctDNA levels were significantly correlated with LDH concentrations ( p=0.049, r=0.5). We further found significantly higher ctDNA concentrations in patients with transformed DLBCL (vs. non-transformed, p=0.008) and in those with 3 or more prior treatment lines (vs. 1-2, p=0.04). At day 7-10 after CAR T-cell therapy, ctDNA levels dropped a median of 10-fold compared to the pretreatment time point ( p=0.008). Patients achieving a complete (CMR) or partial metabolic response (PMR) by PET-CT after 4-6 weeks showed a significant decrease of ctDNA ( p=0.001), while those with a stable (SD) or progressive disease (PD) had no significant ctDNA reduction at day 7-10 after CAR T-cell infusion. Vice versa, while 100% of patients with a >1.5-log-fold drop of ctDNA level between the pretreatment and interim time point revealed a CMR or PMR in early PET-CT scans, only 62% of patients with a smaller ctDNA decline responded to treatment according to PET-CT. Conclusions Our data suggests, together with previously published studies (Sworder BJ et al., Cancer Cell, 2023; Frank MJ et al. J Clin Oncol, 2021), that ctDNA profiling may allow robust noninvasive genotyping, accurate risk-stratification, and early prediction of clinical outcomes in r/r DLBCL patients undergoing CAR T-cell therapy. Although limited by small sample size, this pilot study highlights a potential future role of ctDNA measurements for personalized treatment selection and guidance in clinical trials.

Pattern Analysis of Daily Lesion Volume Trajectories for Early Prediction of Glioblastoma Progression During MR-Linac Radiotherapy

Distinguishing between true progression (TP) and pseudoprogression (PP) post-radiotherapy (RT) is of paramount importance for treatment management of patients with glioblastoma (GBM). MR-Linac systems allow for daily monitoring of tumor changes throughout the course of RT. We hypothesized that the patterns of tumor volume change during RT may enable early prediction of treatment response. Using an IRB-approved prospective cohort of GBM patients undergoing 30 fractions of chemoRT to 60 Gy on a 0.35T MR-Linac, tumor/edema (tumor lesion) regions of interest (ROI) were contoured on daily T2-weighted treatment set-up scans. The obtained tumor lesion (TL) volume changes during treatment were smoothed with a moving average Gaussian window over time. Non-negative Matrix Factorization (NMF) was applied to the data matrix D (N x F), containing the trajectories in its rows for each patient, where N is the number of patients analyzed and F is the number of fractions. NMF represents D as a linear combination of three temporal (hidden) patterns and their weights in each individual trajectory. The same analysis was performed for ΔD, containing the changes in volumes with reference to the first fraction. The calculated weights were scaled in [0, 1], expressed as probabilities (by ℓ1-normalization) and used as features in Linear Discriminant Analysis (LDA). The LDA model was trained to differentiate between no progression (NP), PP and TP, and assessed by leave-one-subject-out cross-validation. Thirty-six patients were screened for inclusion: 9 were excluded due to no T2 lesion (resection cavity only). Of the remaining 27 GBM patients analyzed, 10 had no tumor growth on first post-RT diagnostic MRI, 6 were determined to have PP based on regression or long-term stability of findings, and 11 had TP due to continued progression of disease past 6 months, rapid patient death from disease, or tissue sampling showing active malignancy. With the use of only 2 features, LDA achieved an overall accuracy of 70.4% classifying correctly: 6 (60%), 4 (67%), and 9 (82%) patients with NP, PP, and TP, respectively. The temporal NMF patterns (monotonous decrease, rapid increase during the third part of the treatment, etc.) indicate that there is enough signal to classify patients' response based on the pattern tumor volume changes during RT. We identified tumor dynamics' patterns during RT, indicative of differential behavior of tumor growth between TP and PP. Although with a limited number of patients, these initial results suggest that tumor volume changes during treatment may provide early markers of treatment response. This could allow physicians to adapt/intensify treatment in real time for poorly responding patients. Next steps include automating the process of real-time tumor volume monitoring by incorporating a deep learning solution for automatic volume delineation on daily treatment set-up scans.

Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma

BackgroundAccording to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival. MethodsA total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves. ResultsOf all the models, the random forest (RF) model with peritumor (+10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoff value (0.34) was calculated according to the Youden's index. Patients were then divided into a high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤ 0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260–0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002–1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.001–1.005, P = 0.025), performance status (HR = 2.400, 95% CI: 1.200–4.800, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780–0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416–8.552, P = 0.007). ConclusionsThe radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.

Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer.

This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter. ∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone. The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

MO-0869 The impact of the ADC for the early prediction of treatment response after definitive radiotherapy

MO-0869 The impact of the ADC for the early prediction of treatment response after definitive radiotherapy

Corrigendum: Prediction of early treatment response to the combination therapy of TACE plus lenvatinib and anti-PD-1 antibody immunotherapy for unresectable hepatocellular carcinoma: Multicenter retrospective study

Corrigendum: Prediction of early treatment response to the combination therapy of TACE plus lenvatinib and anti-PD-1 antibody immunotherapy for unresectable hepatocellular carcinoma: Multicenter retrospective study

Abstract P6-01-19: Multiparametric MRI-based Longitudinal-radiomics Analysis for Early Prediction of Treatment Response of Breast Cancers to Neoadjuvant Chemotherapy: A Multicenter Study

Abstract Purpose: We evaluated the performance of the longitudinal-radiomics of multi-parametric magnetic resonance imaging (MRI), developed and validated based on a multicenter dataset adopting a radiomic strategy, for prediction of treatment response to neoadjuvant chemotherapy (NAC) in breast cancer. Methods and materials: We analyzed clinical and pathological data of patients with breast cancer and compared machine-learning classification algorithms in predicting treatment response following NAC from four hospitals (primary cohort and external validation cohort 1-3) between July 2015 and December 2021. Patients were monitored with MRI before (pretreatment) and after (mid-treatment) three or four cycles of NAC. Longitudinal-radiomics analysis was performed at pre- and mid-treatment dynamic contrast-enhanced, T2-weighted imaging, and diffusion-weighted imaging mapping by using 3D Slicer software. Radiomics features were extracted from original and filtered images using PyRadiomics (V3.0.1) filtered images were generated using Laplacian of Gaussian and wavelet filters. The stacking strategy including LR, LDA and SVM (radial basis function) were used to combined the results of the base models, and gave a secondary prediction result. Results: In total, 1048 cases were enrolled in the study. In identifying the residual cancer burden (RCB) 0 v.s. RCB 1-3, longitudinal-radiomic models achieved the highest area under the receiver operating characteristic curve (AUC) of 0.909 and 0.893 in the training and test sets. The accuracies of the stacking-SVM classifier were 84.3% (86/102), 75.6% (205/271) and 77.9% (265/340) in the external validation set. In identifying the RCB 3 v.s. RCB 0-2, longitudinal-radiomic models achieved the highest AUC of 0.981 and 0.923 in the training and test sets. In the external validation set, the performance was with accuracy=91.2%-92.2%. The cascade model also stratified 78.9%, 69.1% and 76.1% of the RCB 1-2 patients into RCB 1-2 prediction group to receive suitable NAC and feasible breast-conserving surgery. Our findings showed high predictive ability and reproducibility across multicenter dataset. By two-step prediction results of cascade models, the patient could be assigned to one of the three groups corresponding to three RCB status: RCB 0, RCB 1-2, or RCB 3. In this way, using the cascade models, some of RCB 3 patients may allow treatment to be tailored for optimal outcomes. RCB 0 and RCB 1-2 groups could receive suitable NAC and feasible breast-conserving surgery. Conclusion: In conclusion, our study had good predictive ability for RCB assessment, which could help clinicians guide individualized treatment options for breast cancer patients in NAC. The application of stacking cascade model may have the potential to further reduce unnecessary NAC and economic costs associated with current breast cancer management. Figure 1. The flowchart of the study. This was a multicenter study with patients retrospectively enrolled from four medical centers in different cities of China. Pretreatment and midtreatment multi-parametric MRI (DCE, T2WI, and DWI) and clinical information were proposed to predict treatment response to NAC in patients with primary invasive breast cancer. The data from Foshan was used as the primary cohort (PC), and the other three data sets were used as independent validation cohorts (VC). This study included radiomic feature engineering, model building, model comparison and clinical application. Figure 1. The flowchart of the study. This was a multicenter study with patients retrospectively enrolled from four medical centers in different cities of China. Pretreatment and midtreatment multi-parametric MRI (DCE, T2WI, and DWI) and clinical information were proposed to predict treatment response to NAC in patients with primary invasive breast cancer. The data from Foshan was used as the primary cohort (PC), and the other three data sets were used as independent validation cohorts (VC). This study included radiomic feature engineering, model building, model comparison and clinical application. Figure 2. ROC curves among different radiomic signatures and among different models in identifying the RCB 0 vs RCB 1-3. RCB, residual cancer burden; ROC, receiver operating characteristic; SVM, support vector machine; RF, random forest; MLP, multilayer perceptron; LR, logistic regression. Figure 2. ROC curves among different radiomic signatures and among different models in identifying the RCB 0 vs RCB 1-3. RCB, residual cancer burden; ROC, receiver operating characteristic; SVM, support vector machine; RF, random forest; MLP, multilayer perceptron; LR, logistic regression. Figure 3. ROC curves among different radiomic signatures and among different models in identifying the RCB 3 vs RCB 0-2. RCB, residual cancer burden; ROC, receiver operating characteristic; SVM, support vector machine; RF, random forest; MLP, multilayer perceptron; LR, logistic regression. Figure 3. ROC curves among different radiomic signatures and among different models in identifying the RCB 3 vs RCB 0-2. RCB, residual cancer burden; ROC, receiver operating characteristic; SVM, support vector machine; RF, random forest; MLP, multilayer perceptron; LR, logistic regression. Citation Format: wei li, Yuhong huang, teng zhu, zhiyong wu, ying lin, guolin ye, Kun Wang. Multiparametric MRI-based Longitudinal-radiomics Analysis for Early Prediction of Treatment Response of Breast Cancers to Neoadjuvant Chemotherapy: A Multicenter Study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-19.

Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent mental health condition and has become a pressing societal challenge. Early prediction of treatment response may aid in the rehabilitation engineering of depression, which is of great practical significance for the relief of suffering and burden of MDD. In this paper, we present a sequence modeling approach that uses data collected by passive sensing techniques to predict patients with an outcome of treatment responded defined by the reduction in clinical administrated scales. Hundreds of patients with MDD have been recruited from outpatient clinics at 4 psychiatric sites. Each has been delivered with a self-developed app to passively record their daily phone usage and physical data with minimal human action. An unavoidable dilemma in passive sensing is missing values. To overcome that, the proposed approach combined feature extraction and sequence modeling methods to fully utilize the pattern of missing values from longitudinal data. With no treatment constraints, it enables us to predict the treatment response of MDD 8-10 weeks before the completion of the treatment course, leaving time for preventative measures. Our work explored the feasibility of treatment response prediction using longitudinal passive sensing data and sparse ground truth, and also has the potential for preventing depression by forecasting treatment outcomes weeks in advance.

Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer.

To build and validate a multi-parametric MRI (mpMRI)-based radiomics nomogram for early prediction of treatment response to neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer. Baseline MRI were retrospectively enrolled from 141 patients with gastric adenocarcinoma who received NAC followed by radical gastrectomy. According to pathologic response of tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) and further divided into a training (n = 85) and validation dataset (n = 56). Radiomics score (Radscore) were built from T2WI, ADC, and venous phase of dynamic-contrasted-enhanced MR imaging. Clinical information, laboratory indicators, MRI parameters, and follow-up data were also recorded. According to multivariable regression analysis, an mpMRI radiomics nomogram was built and its predictive ability was evaluated by ROC analysis. Decision curve analysis was applied to evaluate the clinical usefulness. Kaplan-Meier survival curves based on the nomogram were used to estimate the progression-free survival (PFS) and overall survival (OS) in the validation dataset. Both single sequence-based Radscores and mpMRI radiomics nomogram were associated with pathologic response (p < 0.001). The nomogram achieved the highest diagnostic ability with area under ROC curve of 0.844 (95% CI, 0.749-0.914) and 0.820 (95% CI, 0.695-0.910) in the training and validation datasets. The hazard ratio of the nomogram for PFS and OS prediction was 2.597 (95% CI: 1.046-6.451, log-rank p = 0.023) and 2.570 (95% CI: 1.166-5.666, log-rank p = 0.011). The mpMRI-based radiomics nomogram showed preferable performance in predicting pathologic response to NAC in LAGC. • This study investigated the value of multi-parametric MRI-based radiomics in predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. • The nomogram incorporating T2WI Radscore, ADC Radscore, and DCE Radscore as well as Borrmann classification outperformed the single sequence-based Radscore. • The nomogram also exhibited a promising prognostic ability for patient survival and enriched radiomics studies in gastric cancer.

Predicting the Early Response to Neoadjuvant Therapy with Breast MR Morphological, Functional and Relaxometry Features-A Pilot Study.

Aim: To evaluate the role of MR relaxometry and derived proton density analysis in the prediction of early treatment response after two cycles of neoadjuvant therapy (NAT), in patients with breast cancer. Methods: This was a prospective study that included 59 patients with breast cancer, who underwent breast MRI prior (MRI1) and after two cycles of NAT (MRI2). The MRI1 included a sequential acquisition with five different TE’s (50, 100, 150, 200 and 250 ms) and a TR of 5000 ms. Post-processing was used to obtain the T2 relaxometry map from the MR acquisition. The tumor was delineated and seven relaxometry and proton density parameters were extracted. Additional histopathology data, T2 features and ADC were included. The response to NAT was reported based on the MRI2 as responders: partial response (>30% decreased size) and complete response (no visible tumor stable disease (SD); and non-responders: stable disease or progression (>20% increased size). Statistics was done using Medcalc software. Results: There were 50 (79.3%) patients with response and 13 (20.7%) non-responders to NAT. Age, histologic type, “in situ” component, tumor grade, estrogen and progesterone receptors, ki67% proliferation index and HER2 status were not associated with NAT response (all p > 0.05). The nodal status (N) 0 was associated with early response, while N2 was associated with non-response (p = 0.005). The tumor (T) and metastatic (M) stage were not statistically significant associated with response (p > 0.05). The margins, size and ADC values were not associated with NAT response (p-value > 0.05). The T2 min relaxometry value was associated with response (p = 0.017); a cut-off value of 53.58 obtained 86% sensitivity (95% CI 73.3−94.2), 69.23 specificity (95% CI 38.6−90.9), with an AUC = 0.715 (p = 0.038). The combined model (T2 min and N stage) achieved an AUC of 0.826 [95% CI: 0.66−0.90, p-value < 0.001]. Conclusions: MR relaxometry may be a useful tool in predicting early treatment response to NAT in breast cancer patients.

Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study.

SummaryBackgroundEarly prediction of treatment response to neoadjuvant chemotherapy (NACT) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a Siamese multi-task network (SMTN) for predicting pathological complete response (pCR) based on longitudinal ultrasound images at the early stage of NACT.MethodsIn this multicentre, retrospective cohort study, a total of 393 patients with biopsy-proven HER2-positive breast cancer were retrospectively enrolled from three hospitals in china between December 16, 2013 and March 05, 2021, and allocated into a training cohort and two external validation cohorts. Patients receiving full cycles of NACT and with surgical pathological results available were eligible for inclusion. The key exclusion criteria were missing ultrasound images and/or clinicopathological characteristics. The proposed SMTN consists of two subnetworks that could be joined at multiple layers, which allowed for the integration of multi-scale features and extraction of dynamic information from longitudinal ultrasound images before and after the first /second cycles of NACT. We constructed the clinical model as a baseline using multivariable logistic regression analysis. Then the performance of SMTN was evaluated and compared with the clinical model.FindingsThe training cohort, comprising 215 patients, were selected from Yunnan Cancer Hospital. The two independent external validation cohorts, comprising 95 and 83 patients, were selected from Guangdong Provincial People's Hospital, and Shanxi Cancer Hospital, respectively. The SMTN yielded an area under the receiver operating characteristic curve (AUC) values of 0.986 (95% CI: 0.977–0.995), 0.902 (95%CI: 0.856–0.948), and 0.957 (95%CI: 0.924–0.990) in the training cohort and two external validation cohorts, respectively, which were significantly higher than that those of the clinical model (AUC: 0.524–0.588, Pall < 0.05). The AUCs values of the SMTN within the anti-HER2 therapy subgroups were 0.833-0.972 in the two external validation cohorts. Moreover, 272 of 279 (97.5%) non-pCR patients (159 of 160 (99.4%), 53 of 54 (98.1%), and 60 of 65 (92.3%) in the training and two external validation cohorts, respectively) were successfully identified by the SMTN, suggesting that they could benefit from regime adjustment at the early-stage of NACT.InterpretationThe SMTN was able to predict pCR in the early-stage of NACT for HER2-positive breast cancer patients, which could guide clinicians in adjusting treatment regimes.FundingKey-Area Research and Development Program of Guangdong Province (No.2021B0101420006); National Natural Science Foundation of China (No.82071892, 82171920); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No.2022B1212010011); the National Science Foundation for Young Scientists of China (No.82102019, 82001986); Project Funded by China Postdoctoral Science Foundation (No.2020M682643); the Outstanding Youth Science Foundation of Yunnan Basic Research Project (202101AW070001); Scientific research fund project of Department of Education of Yunnan Province(2022J0249). Science and technology Projects in Guangzhou (202201020001;202201010513); High-level Hospital Construction Project (DFJH201805, DFJHBF202105).

Abnormal spontaneous neural activity in hippocampal-cortical system of patients with obsessive-compulsive disorder and its potential for diagnosis and prediction of early treatment response.

Early brain functional changes induced by pharmacotherapy in patients with obsessive–compulsive disorder (OCD) in relation to drugs per se or because of the impact of such drugs on the improvement of OCD remain unclear. Moreover, no neuroimaging biomarkers are available for diagnosis of OCD and prediction of early treatment response. We performed a longitudinal study involving 34 patients with OCD and 36 healthy controls (HCs). Patients with OCD received 5-week treatment with paroxetine (40 mg/d). Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were applied to acquire and analyze the imaging data. Compared with HCs, patients with OCD had higher ReHo values in the right superior temporal gyrus and bilateral hippocampus/parahippocampus/fusiform gyrus/cerebellum at baseline. ReHo values in the left hippocampus and parahippocampus decreased significantly after treatment. The reduction rate (RR) of ReHo values was positively correlated with the RRs of the scores of Yale–Brown Obsessive–Compulsive Scale (Y-BOCS) and obsession. Abnormal ReHo values at baseline could serve as potential neuroimaging biomarkers for OCD diagnosis and prediction of early therapeutic response. This study highlighted the important role of the hippocampal–cortical system in the neuropsychological mechanism underlying OCD, pharmacological mechanism underlying OCD treatment, and the possibility of building models for diagnosis and prediction of early treatment response based on spontaneous activity in the hippocampal–cortical system.

T2 relaxation time for the early prediction of treatment response to chemoradiation in locally advanced rectal cancer

ObjectivesPoor responders to chemoradiotherapy (CRT) for locally advanced rectal cancer (LARC) can still have a good prognosis if the treatment strategy is changed in time. However, no reliable predictor of early-treatment response has been identified. The purpose of this study was to investigate the role of T2 relaxation time in magnetic resonance imaging (MRI) for the early prediction of a pathological response to CRT in LARC.MethodsA total of 123 MRIs were performed on 41 LARC patients immediately before, during, and after CRT. The corresponding tumor volume, T2 relaxation time, and apparent diffusion coefficient (ADC) values at different scan time points were obtained. The Mann–Whitney U test was used to compare the T2 relaxation time between pathological good responders (GR) and non-good responders (non-GR). The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter in predicting tumor response to CRT.ResultsTwenty-one (51%) and 20 (49%) were GRs and non-GRs, respectively. T2 relaxation time showed an excellent intraclass correlation coefficient (ICC) of > 0.85 at three-time points. It was significantly lower in the GR group than in the non-GR group during and after CRT. The early T2 decrease had a high AUC of 0.91 in differentiating non-GRs and GRs, similar to 0.90 of the T2 value after CRT.ConclusionsT2 relaxation time may help predict treatment response to CRT for LARC earlier, rather than having to wait until the end of CRT, thereby alleviating the physical burden for patients with no good response.

16P Assessing early changes in plasma HER2 levels is useful for predicting therapeutic response in advanced breast cancer: A multicenter, prospective, noninterventional clinical study

Early prediction of treatment response is crucial for the optimal treatment of advanced breast cancer. We aimed to explore whether monitoring early changes in plasma human epidermal growth factor receptor 2 (HER2) levels using digital PCR (dPCR) could predict the treatment response in advanced breast cancer.

Abnormal spontaneous neural activity as a potential predictor of early treatment response in patients with obsessive–compulsive disorder

BackgroundWe aimed to explore the value of early improvement in obsessive–compulsive disorder (OCD) along with potential imaging changes after treatment with paroxetine in building diagnostic models and predicting treatment response. MethodsThe clinical symptoms of patients with OCD were assessed at baseline and post-treatment (four weeks). Resting-state functional magnetic resonance imaging, fractional amplitudes of low-frequency fluctuations (fALFF) indicator, support vector machine (SVM), support vector regression (SVR), and correlation analysis were performed to acquire and analyze the data. ResultsIn comparison with healthy controls, OCD patients at baseline had abnormal fALFF in several brain regions. The abnormal fALFF in the left precuneus/ posterior cingulate cortex (PCC) (r = −0.526, p = 0.001) and right middle cingulate cortex (MCC) (r = −0.588, p < 0.001) were negatively correlated with the severity of compulsions. Patients with OCD showed significantly clinical improvement along with significantly decreased fALFF in the left precuneus after treatment. The SVM analysis showed that the classifier had an accuracy of 90.00% based on the fALFF in the right precentral gyrus and right MCC at baseline. The SVR analysis showed that the actual remission of OCD was positively correlated with the predicted remission based on the fALFF in the left precuneus/PCC and right MCC at baseline. LimitationsThis monocentric study with the relatively small sample size might restrict the generalizability of the results to other centers. ConclusionsAbnormal spontaneous neural activities in patients with OCD could serve as potential neuroimaging biomarkers for diagnosis and prediction of early treatment response.

Comparison of MRI-based response criteria and radiomics for the prediction of early response to transarterial radioembolization in patients with hepatocellular carcinoma

PurposeThe purpose of this study was to evaluate the capabilities of radiomics using magnetic resonance imaging (MRI) data in the assessment of treatment response to 90yttrium transarterial radioembolization (TARE) in patients with locally advanced hepatocellular carcinoma (HCC) by comparison with predictions based on European Association for the Study of the Liver (EASL) criteria. Patients and methodsTwenty-two patients with HCC (19 men, 3 women; mean age: 66.7 ± 9.8 [SD]; age range: 37–82 years) who underwent contrast-enhanced MRI 4 ± 1 weeks before and 4 ± 4 weeks after TARE, were enrolled in this retrospective study. Regions of interest were placed manually along the contours of the treated tumor on each axial slice of arterial and portal phase images using the ITK-SNAP post-processing software. For each MRI, the Pyradiomics Python package was used to extract 107 radiomics features on both arterial and portal phases, and resulting delta-features were computed. The Mann-Whitney U test with Bonferroni correction was used to select statistically different features between responders and non-responders (i.e., those with progressive or stable disease) at 6-month follow-up, according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Finally, for each selected feature, univariable logistic regression with leave-one-out cross validation procedure was used to perform receiver operating characteristic (ROC) curve analysis and compare radiomics parameters with MRI variables. ResultsAccording to mRECIST, 14 patients (14/22; 64%) were non-responders and 8 (8/22; 36%) were responders. Four radiomics parameters (long run emphasis, minor axis length, surface area, and gray level non-uniformity on arterial phase images) were the only predictors of early response. ROC curve analysis showed that long run emphasis was the best parameter for predicting early response, with 100% sensitivity (95% CI: 68–100) and 100% specificity (95% CI: 78–100). EASL morphologic criteria yielded 75% sensitivity (95% CI: 41–96%) and 93% specificity (95% CI: 69–100%). ConclusionRadiomics allows identify marked differences between responders and non-responders, and could aid in the prediction of early treatment response following TARE in patients with HCC.

Early prediction of treatment response in rheumatoid arthritis by quantitative macrophage PET

ObjectiveTo determine whether macrophage positron emission tomography (PET)/computed tomography (CT) imaging using (R)-[11C]PK11195 at 0 and 2 weeks is associated with clinical response at 13 weeks in patients with early...

Dynamic contrast-enhanced magnetic resonance imaging for evaluating early response to radiosurgery in patients with vestibular schwannoma.

This study aimed to use dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate early treatment response in vestibular schwannoma (VS) patients after radiosurgery. Twenty-four VS patients who underwent gamma knife radiosurgery were prospectively followed up for at least four years. DCE-MRI sequences, in addition to standard MRI protocol, were obtained prior to radiosurgery, at 3 and 6months. Conventionally, treatment responses based on tumor volume changes were classified as regression or stable (RS), transient tumor enlargement (TTE), and continuous tumor enlargement (CTE). DCE-MRI parameters, such as Ktrans, Kep and Ve, were compared according to follow-up periods and between groups. The diagnostic performance was tested using receiver operating characteristic (ROC) curves. Changes in tumor volume were as follows at the last 48 months of follow-up: RS in 11 patients (45.8%), TTE in 10 patients (41.7%), and CTE in three patients (12.5%). The median time required to distinguish TTE from CTE using conventional MRI was 12months (range 9-18). The Ktrans and Ve were significantly decreased in patients with RS and TTE at 3 and 6months, but did not differ significantly in patients with CTE. There were no significant differences in Ktrans and Ve between patients with RS and TTE at 3 and 6months. Both Ktrans and Ve demonstrated high diagnostic performance in evaluating early treatment response to radiosurgery in patients with VS. DCE-MRI may aid in the monitoring and early prediction of treatment response in patients with VS following radiosurgery.

Association of early changes of circulating cancer stem-like cells with survival among patients with metastatic breast cancer

Background:This study aimed to investigate the role of circulating tumor cells (CTCs) and circulating cancer stem-like cells (cCSCs) before and after one cycle of chemotherapy and assessed the effects of early changes in CTCs and cCSCs on the outcomes of patients with metastatic breast cancer.Methods:Patients with stage IV invasive ductal carcinoma of the breast who received first-line chemotherapy between April 2014 and January 2016 were enrolled. CTCs and cCSCs were measured before the first cycle of chemotherapy (baseline) and on day 21, before the second cycle of chemotherapy commenced; a negative selection strategy and flow cytometry protocol were employed.Results:CTC and cCSC counts declined in 68.8 and 45.5% of patients, respectively. Declines in CTCs and cCSCs following the first chemotherapy cycle were associated with superior chemotherapy responses, longer progression-free survival (PFS), and longer overall survival (OS). An early decline in cCSCs remained an independent prognostic indicator for OS and PFS in multivariate analysis.Conclusions:A cCSC decline after one cycle of chemotherapy for metastatic breast cancer is predictive of a superior chemotherapy response and longer PFS and OS, implying that cCSC dynamic monitoring may be helpful in early prediction of treatment response and prognosis.