Prediction Of Pathologic Response Research Articles

Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features

Establishing predictive models for the pathological response and lymph node metastasis in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT) based on MRI radiomic features derived from the tumor and mesorectal compartment (MC). This study included 209 patients with LARC who underwent rectal MRI both before and after nCRT. The patients were divided into a training set (n = 146) and a test set (n = 63). Regions of interest (ROIs) for the tumor and MC were delineated on both pre- and post-nCRT MRI images. Radiomic features were extracted, and delta radiomic features were computed. The predictive endpoints were pathological complete response (pCR), pathological good response (pGR), and lymph node metastasis (LNM). Feature selection for various models involved sequentially removing features with a correlation coefficient > 0.9, and features with P-values ≥ 0.05 in univariate analysis, followed by LASSO regression on the remaining features. Logistic regression models were developed, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC). Among the 209 LARC patients, the number of patients achieving pCR, pGR, and LNM were 44, 118, and 40, respectively. The optimal model for predicting each endpoint is the combined model that incorporates pre- and delta-radiomics features for both the tumor and MC. These models exhibited superior performance with AUC values of 0.874 (for pCR), 0.801 (for pGR), and 0.826 (for LNM), outperforming the MRI tumor regression grade (mrTRG) which yielded AUC values of 0.800, 0.715, and 0.603, respectively. The results demonstrate the potential utility of the tumor and MC radiomics features, in predicting treatment efficacy among LARC patients undergoing nCRT.

The contrast-free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.

Combined EORTC FDG-PET/CT and RECIST 1.1 response criteria and prediction of pathological response to neoadjuvant nivolumab and ipilimumab for clinical stage III melanoma.

9590 Background: Accurate identification of pathological response to neoadjuvant therapy for clinical stage III melanoma before surgery may allow us to better tailor the treatment. RECIST 1.1 has shown poor concordance rates with pathological responses and EORTC FDG-PET/CT response criteria using SUV max is susceptible to internal and external variables leading to difficulty in determining accuracy appropriately. Methods: We conducted a multicenter retrospective analysis of patients with clinical stage III melanoma treated with neoadjuvant nivolumab 3mg/kg + ipilimumab 1mg/kg(N3+I1) for two cycles who did baseline and pre-operative 18F-FDG-PET/CT. RECIST 1.1 and EORTC PETCT criteria were used to classify patients in terms of response. The pathological results were correlated to the image findings. Results: Between Jan 2019 and Jan 2024, 37 patients with clinical stage III melanoma treated with neoadjuvant N3+I1 were identified. Gender: 25 males (67%), median age (years): 58 (range: 31-78), BRAFV600 mut: 25(67%), positive nodes on baseline exam:1=30 (81%), 2=4(11%), and 3 or more =3(8%). Stage AJCC 8th edition: IIIB=23 (62%), IIIC=12 (32%), IIID=2(6%). Among them, 34 had baseline and post-neoadjuvant therapy PET-CT. Twenty-one (62%) had major pathological response (MPR) and 13(38%) had non-MPR. Using objective response and non-objective criteria to capture MPR and non-MPR, respectively, we found the following results: RECIST 1.1 (sensitivity= 48%, specificity=100%, accuracy=68%), EORTC-PET (sensitivity=90%, specificity=69%, accuracy=82%). Combining RECIST 1.1 and EORTC-PET/CT analysis, we found that: double objective responders (CR/PR and CMR/PMR, n=10) had 100% specificity to MPR and double progressors (PD/PMD, n=9) had 95% specificity to non-MPR. The only patient classified as double progressor that did not have non-MPR presented an intense inflammatory response with fever, pain and erythema leading to an increase in the size and number of lymph-nodes. No viable cells were found in the pathological report. Conclusions: Using combined RECIST 1.1 and EORTC-PET/CT response criteria may help to predict pathological response in patients with clinical stage III melanoma who undergo to neoadjuvant nivolumab and ipilimumab.

MRI and RNA-seq fusion for prediction of pathological response to neoadjuvant chemotherapy in breast cancer

Accurate prediction of the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is crucial for precise treatment of breast cancer. However, current studies mainly rely on single-modal data, with limited studies focusing on multimodal data. In this study, we developed and validated a deep learning-based multimodal fusion model that predicts the response of breast tumor to NAC by integrating multi-parametric magnetic resonance imaging (MRI) and RNA sequencing (RNA-seq) information related to breast tumor. For comparison, we separately built four single-modal models with either MR images or RNA-seq data. Moreover, our approach has demonstrated better performance in integrating MR images and RNA-seq data. The average accuracy is 90.20% and area under the receiver operating characteristic curve (AUC) is 0.936 for our model. These findings indicate that our proposed approach has achieved higher accuracy in predicting the pathological response to NAC.

Abstract 6412: Combination of liquid biopsy and PET/CT enhances prediction of pathological response to neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma

Abstract Background: Neoadjuvant immunochemotherapy (NICT) demonstrated remarkable efficacy in esophageal squamous cell carcinoma (ESCC) patients. An accurate evaluation of treatment response is critical before surgery. Therefore, this study aimed to explore and evaluate the use of non-invasive and clinical assessments to predict pathological response to NICT prior to surgery in these patients. Methods: A total of 71 ESCC patients who received NICT between March 2020 and February 2022 were recruited in this study. Clinicopathological characteristics and PET/CT assessment before and after NICT were evaluated. Peripheral blood mononuclear cells (PBMCs) were prospectively collected for genomic and T cell receptor (TCR) profiling using targeted next-generation sequencing and multiplex PCR, respectively. Univariate analysis was performed to identify features associated with pathological complete response (pCR) of primary tumor. Optimal predictive features were identified using the least absolute shrinkage and selection operator (LASSO) regression model. Subsequently, a prediction model was established using logistic regression, followed by receiver operating characteristic (ROC) analysis and internal validation. Results: Univariate analyses revealed significant associations between pCR and post-NICT features, including ctDNA, PET/CT parameters, and TCR clonality and diversity. Optimal predictive features were identified as post-NICT SUVmax, ctDNA positivity, and TCR clonotype clonality, and their area under the ROC curve (AUC) was 0.784 (95%CI: 0.644-0.924), 0.739 (95%CI: 0.664-0.813), and 0.682 (95%CI: 0.535-0.829), respectively. The final prediction model integrating these three features achieved an AUC of 0.918 (95%CI: 0.849-0.986), an accuracy of 0.768 (95%CI: 0.661-0.875), a sensitivity of 1.000 (95%CI: 1.000-1.000), and a specificity of 0.705 (95%CI: 0.568-0.841), indicating excellent predictive performance. Conclusion: The combination of post-therapy ctDNA status, TCR clonality, and PET/CT assessments showed robust predictive power for assessing pathological response to NICT in ESCC, suggesting the potential to inform clinical decision-making for these patients. Future large-scale studies are warranted to strengthen and verify the findings from our study. Citation Format: Weixiong Yang, Zengli Fang, Xiaoyan Wang, Hui Luo, Shuishen Zhang, Bo Zeng, Zhenguo Liu, Chenxuan Wang, Qiuxiang Ou, Lingling Yang, Haimeng Tang, Sai-Ching J. Yeung, Chao Cheng. Combination of liquid biopsy and PET/CT enhances prediction of pathological response to neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6412.

Spectral CT vs. diffusion-weighted imaging for the quantitative prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer.

To compare the performance of spectral CT and diffusion-weighted imaging (DWI) for predicting pathologic response after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC). This was a retrospective analysis drawn from a prospective dataset. Sixty-five patients who underwent baseline concurrent triple-phase enhanced spectral CT and DWI-MRI and standard NAC plus radical gastrectomy were enrolled, and those with poor images were excluded. The tumor regression grade (TRG) was the reference standard, and patients were classified as responders (TRG 0 + 1) or non-responders (TRG 2 + 3). Quantitative iodine concentration (IC), normalized IC (nIC), and apparent diffusion coefficient (ADC) were measured by placing a freehand region of interest manually on the maximal two-dimensional plane. Their differences between responders and non-responders were compared. The performances of significant parameters were evaluated by the receiver operating characteristic analysis. The correlations between parameters and TRG status were explored through Spearman correlation coefficient test. Kaplan-Meier survival analysis was adopted to analyze their relationship with patient survival. nICDP and ADC were associated with the TRG and yielded comparable performances for predicting TRG categories, with area under the curve (AUC) of 0.674 and 0.673, respectively. Their combination achieved a significantly increased AUC of 0.770 (p ; 0.05) and was associated with patient disease-free survival, with hazard ratio of 2.508 (1.043-6.029). Spectral CT and DWI were equally useful imaging techniques for predicting pathologic response to NAC in LAGC. The combination of nICDP and ADC gained significant incremental benefits and was related to patient disease-free survival. Spectral CT and DWI-based quantitative measurements are effective markers for predicting the pathologic regression outcomes of locally advanced gastric cancer patients after neoadjuvant chemotherapy. • The pathologic tumor regression grade, the standard criteria for treatment response after neoadjuvant chemotherapy in gastric cancer patients, is difficult to predict early. • The quantitative parameters of normalized iodine concentration at delay phase and apparent diffusion coefficients were correlated with pathologic response; their combination demonstrated incremental benefits and was associated with patient disease-free survival. • Spectral CT and DWI are equally useful imaging modalities for predicting tumor regression grade after neoadjuvant chemotherapy in patients with locally advanced gastric cancer.

Early prediction of pathological response to neoadjuvant chemotherapy of breast tumors: a comparative study using amide proton transfer-weighted, diffusion weighted and dynamic contrast enhanced MRI.

To examine amide proton transfer-weighted (APTw) combined with diffusion weighed (DWI) and dynamic contrast enhanced (DCE) MRI for early prediction of pathological response to neoadjuvant chemotherapy in invasive breast cancer. In this prospective study, 50 female breast cancer patients (49.58 ± 10.62 years old) administered neoadjuvant chemotherapy (NAC) were enrolled with MRI carried out both before NAC (T0) and at the end of the second cycle of NAC (T1). The patients were divided into 2 groups based on tumor response according to the Miller-Payne Grading (MPG) system. Group 1 included patients with a greater degree of decrease in major histologic responder (MHR, Miller-Payne G4-5), while group 2 included non-MHR cases (Miller-Payne G1-3). Traditional imaging protocols (T1 weighted, T2 weighted, diffusion weighted, and DCE-MRI) and APTw imaging were scanned for each subject before and after treatment. APTw value (APTw0 and APTw1), Dmax (maximum diameter, Dmax0 and Dmax1), V (3D tumor volume, V0 and V1), and ADC (apparent diffusion coefficient, ADC0 and ADC1) before and after treatment, as well as changes between the two times points (ΔAPT, ΔDmax, ΔV, ΔADC) for breast tumors were compared between the two groups. APT0 and APT1 values significantly differed between the two groups (p = 0.034 and 0.01). ΔAPTw values were significantly lower in non-MHR tumors compared with MHR tumors (p = 0.015). ΔDmax values were significantly higher in MHR tumors compared with non-MHR tumors (p = 0.005). ADC0 and ADC1 values were significantly higher in MHR tumors than in non-MHR tumors (p = 0.038 and 0.035). AUC (Dmax+DWI + APTw) = AUC (Dmax+APTw) > AUC (APTw) > AUC (Dmax+DWI) > AUC (Dmax). APTw imaging along with change of tumor size showed a significant potential in early prediction of MHR for NAC treatment in breast cancer, which might allow timely regimen refinement before definitive surgical treatment.

The role of FDG PET/CT radiomics in the prediction of pathological response to neoadjuvant treatment in patients with esophageal cancer.

Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer. A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers. Included patients had a baseline FDG PET/CT scan and underwent Chemoradiotherapy for Oesophageal Cancer Followed by Surgery Study (CROSS) protocol followed by surgery. Four demographic variables and 107 PET radiomic features were extracted and analyzed using univariate and multivariate analyses to predict response to neoadjuvant therapy. Overall, 53 FDG-avid primary esophageal cancer lesions were segmented and radiomic features were extracted. Seventeen radiomic features and 2 non-radiomics variables were found to exhibit significant differences between neoadjuvant therapy responders and non-responders. An unsupervised hierarchical clustering analysis using these 19 variables classified patients in a manner significantly associated with response to neoadjuvant treatment (p < 0.01). Our findings highlight the potential of FDG PET/CT radiomic features as a predictor for the response to neoadjuvant therapy in esophageal cancer patients. The combination of these radiomic features with select non-radiomic variables provides a model for stratifying patients based on their likelihood to respond to neoadjuvant treatment.

Value of 68Ga-FAPI-04 and 18F-FDG PET/CT in Early Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

This prospective study investigated whether PET parameters from 18F-FDG and 68Ga-fibroblast activation protein inhibitor (FAPI)-04 PET/CT can predict a pathologic response to neoadjuvant chemotherapy (NAC) early in patients with locally advanced gastric cancer (LAGC). Methods: The study included 28 patients with LAGC who underwent 18F-FDG PET/CT and 68Ga-FAPI-04 PET/CT at baseline and after 1 cycle of NAC. PET parameters including SUV and tumor-to-background ratio (TBR), as well as the change rate of SUV and TBR, were recorded. Patients were classified as major or minor pathologic responders according to postoperative pathology findings. We compared the PET parameters between the 2 pathologic response groups and different treatment regimens and analyzed their predictive performance for tumor pathologic response. Results: Major pathologic responders had significantly lower 68Ga-FAPI change rates (percentage SUVmax [%SUVmax], percentage SUVpeak [%SUVpeak], and percentage TBR [%TBR]) than minor pathologic responders. Among the PET parameters, 68Ga-FAPI %SUVmax (area under the curve, 0.856; P = 0.009), %SUVpeak (area under the curve, 0.811; P = 0.022), and %TBR (area under the curve, 0.864; P = 0.007) were significant parameters for early prediction of pathologic response to NAC in LAGC; they had the same predictive accuracy of 89.29%, with the thresholds of decrease to at least 52.43%, 60.46%, and 52.96%, respectively. In addition, 68Ga-FAPI %SUVmax and %TBR showed significant differences between the different treatment regimens. Conclusion: In this preliminary study, 68Ga-FAPI-04 PET change rate parameters were preferable to 18F-FDG in predicting pathologic response to NAC at an early stage in LAGC. 68Ga-FAPI %SUVmax and %TBR may be better predictors of therapeutic response between different treatment regimens. These findings may help optimize the treatment for patients with LAGC.

Non-linear Modifications Enhance Prediction of Pathological Response to Pre-operative PD-1 Blockade in Lung Cancer: A Longitudinal Hybrid Radiological Model

Major pathologic remission (MPR, residual tumor <10%) is a promising clinical endpoint for prognosis analysis in patients with lung cancer receiving pre-operative PD-1 blockade therapy. Most of the current biomarkers for predicting MPR such as PD-L1 and tumor mutation burden (TMB) need to be obtained invasively. They cannot overcome the spatiotemporal heterogeneity or provide dynamic monitoring solutions. Radiomics and artificial intelligence (AI) models provide a practical tool enabling non-invasive follow-up observation of tumor structural information through high-throughput data analysis. Currently, AI-based models mainly focus on the single baseline scan or pipeline, namely sole radiomics or deep learning (DL). This work merged the delta-radiomics based on the slope of classic radiomics indexes within a time interval and the features extracted by deep networks from the subtraction between the baseline and follow-up images. The subtracted images describing the tumor changes were based on the transformation generated by registration. Stepwise optimization of components was performed by repeating experiments among various combinations of DL networks, registration methods, feature selection algorithms, and classifiers. The optimized model could predict MPR with a cross-validation AUC of 0.91 and an external validation AUC of 0.85. A core set of 27 features (eight classic radiomics, 15 delta-radiomics, one classic DL features, and three delta-DL features) was identified. The changes in delta-radiomics indexes during the treatment were fitted with mathematic models. The fitting results revealed that over half of the features were of non-linear dynamics. Therefore, non-linear modifications were made on eight features by replacing the original features with non-linear fitting parameters, and the modified model achieved an improved power. The dynamic hybrid model serves as a novel and promising tool to predict the response of lesions to PD-1 blockade, which implies the importance of introducing the non-linear dynamic effects and DL approaches to the original delta-radiomics in the future.

68Ga-Labeled Fibroblast Activation Protein Inhibitor PET/CT for the Early and Late Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Prospective Study.

68Ga-labeled fibroblast activation protein inhibitor (68Ga-FAPI) PET/CT has demonstrated promising clinical results, with a higher SUVmax and tumor-to-background ratio (TBR) in breast cancer (BC) patients than 18F-FDG PET/CT. Here, we aimed to evaluate the suitability of 68Ga-FAPI PET/CT for the early and late prediction of the pathologic response to neoadjuvant chemotherapy (NAC) in BC. Methods: Twenty-two consecutive patients with newly diagnosed BC and an indication for NAC were prospectively included. All patients underwent standard chemotherapy and 68Ga-FAPI PET/CT at baseline, after 2 cycles of NAC (PET2), and 1 wk before surgery (PET3). SUVmax was measured in the primary tumor region and positive regional lymph nodes. The expression of fibroblast activation protein in the primary lesion was analyzed by immunohistochemistry. Results: Seven patients (31.8%) achieved a pathologic complete response (pCR), and 15 (68.2%) had residual tumors. Thirteen patients (59.1%) showed concentric withdrawal of the primary tumor, and 9 (40.9%) showed diffuse withdrawal. Between PET2 and PET3, the ΔSUVmax of the primary tumor (R 2 = 0.822; P = 0.001) and metastatic lymph nodes (R 2 = 0.645; P = 0.002) were significantly correlated. The absolute values of SUVmax and TBR at PET2 and PET3 were lower in patients with pCR than in those without pCR (P < 0.05). Moreover, a larger ΔSUVmax at any time point was strongly associated with pCR (P < 0.05). Similar downward trends in SUVmax, TBR, and ΔSUVmax were observed in the pattern of primary tumor reduction. For predicting pCR, the optimal cutoff values for ΔSUVmax after 2 chemotherapy cycles, ΔSUVmax before surgery, TBR after 2 chemotherapy cycles, and TBR before surgery of the primary tumor were 3.4 (area under the curve [AUC], 0.890), 1.1 (AUC, 0.978), -63.8% (AUC, 0.879), -90.8% (AUC, 0.978), 7.6 (AUC, 0.848), and 1.4 (AUC, 0.971), respectively. Immunohistochemistry showed that the SUVmax and TBR of 68Ga-FAPI PET/CT were positively correlated with fibroblast activation protein expression (P < 0.001 for both). Conclusion: Assessment of early changes in 68Ga-FAPI uptake during NAC by 68Ga-FAPI PET/CT can predict pCR and primary tumor concentric withdrawal in BC patients. 68Ga-FAPI PET/CT has great potential for the early and late prediction of the pathologic response to NAC in BC.

Utility of contrast-enhanced harmonic endoscopic ultrasonography for prediction of pathological response after neoadjuvant chemotherapy in patients with pancreatic cancer

ObjectivesContrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) plays an important role in the diagnosis of pancreatic lesions. The aim of this study was to evaluate whether CH-EUS is useful for predicting the treatment efficacy of neoadjuvant chemotherapy (NAC) determined by pathological response. MethodsPatients who underwent CH-EUS before chemotherapy and surgical resection were divided into two groups according to poor (group-P) or rich tumor vascularity (group-R) determined by enhancement pattern on early- and late-phase CH-EUS. The pathological response to chemotherapy was categorized according to Evans’ classification. Pathological analysis showing tumor cell destruction (>50 %) defined a good response. ResultsEarly-phase CH-EUS classified 44 patients into group-R and 50 into group-P, whereas late-phase CH-EUS classified 10 into group-R and 84 into group-P. Early-phase CH-EUS classification resulted in significantly higher numbers of patients with a good response in the rich group (n = 19) than in the poor group (n = 4; P = 0.0015). Multivariate analysis showed that assignment to the rich group was the strongest independent factor associated with chemosensitivity (P = 0.006, hazard ratio = 5.66, 95 % confidence interval: 1.17–19.27). In resectable patients, the enhancement pattern was the only independent factor associated with chemosensitivity (group-P vs. group-R, P = 0.003; HR [95 % CI], 14.59 [1.38–154.38]). Late-phase CH-EUS did not reveal a significant difference between group-P and group-R. ConclusionsEvaluation of vascular pattern on CH-EUS could be useful for predicting the efficacy of NAC in patients with pancreatic cancer. The enhancement pattern on CH-EUS could be a one of the useful features for determining NAC indications in resectable pancreatic cancer patients.

357P Prediction of pathologic response to neoadjuvant chemotherapy (NAC) using diffuse optical breast scanner (DOB-Scan) in patients with locally advanced breast cancer (LABC)

357P Prediction of pathologic response to neoadjuvant chemotherapy (NAC) using diffuse optical breast scanner (DOB-Scan) in patients with locally advanced breast cancer (LABC)

Effect of Microsatellite Status and Pan-Immune-Inflammation Score on Pathological Response in Patients with Clinical Stage III Stomach Cancer Treated with Perioperative Chemotherapy.

Background and Objective: This study evaluated the relationship between microsatellite status (MSI) and pan-immune-inflammation score (PIV) in tumor response to neoadjuvant chemotherapy (NAC) in patients with clinical stage III gastric cancer (cStage III GC). Materials and Methods: Microsatellite instability (MSI) status was evaluated based on pathology preparations. Pan-immune-inflammation score (PIV) was obtained from pre-treatment blood tests. The relationship of both parameters with pathological complete response (pCR) was evaluated. Results: A total of 104 patients were included in this study. All the patients were stage III GC patients receiving perioperative treatment. There were 13 patients in total who achieved a pCR response. While CNS was detected in 11 of the patients who achieved a pCR, the MSI status of the other two patients was unknown. No pCR was observed in any patient with MSI-H. According to the cut-off value for PIV, 25 (24%) patients were in the PIV-low (≤53.9) group, while 79 (76%) were in the PIV-high (>53.9) group. Based on univariate analysis, a higher PIV was associated with worse outcomes for pathological response, disease recurrence, and survival (p < 0.05). Conclusions: In patients with clinically stage III GC, the presence of MSI-H may predict no benefit from perioperative treatment. Conversely, a pre-treatment PIV score using specific cut-off values may provide a positive prediction of pathological response and survival.

Enhancing Prediction for Tumor Pathologic Response to Neoadjuvant Immunochemotherapy in Locally Advanced Esophageal Cancer by Dynamic Parameters from Clinical Assessments.

To develop accurate and accessible prediction methods for assessing pathologic response following NICT prior to surgery, we conducted a retrospective study including 137 patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery after two cycles of NICT between January 2019 and March 2022 at our center. We collected clinical parameters to evaluate the dynamic changes in the primary tumor. Univariate and multivariate analyses were performed to determine the correlations between these parameters and the pathologic response of the primary tumor. Subsequently, we constructed prediction models for pCR and MPR using multivariate logistic regression. The MPR prediction Model 2 was internally validated using bootstrapping and externally validated using an independent cohort from our center. The univariate logistic analysis revealed significant differences in clinical parameters reflecting tumor regression among patients with varying pathologic responses. The clinical models based on these assessments demonstrated excellent predictive performance, with the training cohort achieving a C-index of 0.879 for pCR and 0.912 for MPR, while the testing cohort also achieved a C-index of 0.912 for MPR. Notably, the MPR prediction Model 2, with a threshold cut-off of 0.74, exhibited 92.7% specificity and greater than 70% sensitivity, indicating a low rate of underestimating residual tumors. In conclusion, our study demonstrated the high accuracy of clinical assessment-based models in pathologic response prediction, aiding in decision-making regarding organ preservation and radiotherapy adjustments after induction immunochemotherapy.

Ultrasound and clinicopathological characteristics-based model for prediction of pathologic response to neoadjuvant chemotherapy in HER2-positive breast cancer: a case–control study

BackgroundThe objective of this study was to develop a model combining ultrasound (US) and clinicopathological characteristics to predict the pathologic response to neoadjuvant chemotherapy (NACT) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer.Materials and methodsThis is a retrospective study that included 248 patients with HER2-positive breast cancer who underwent NACT from March 2018 to March 2022. US and clinicopathological characteristics were collected from all patients in this study, and characteristics obtained using univariate analysis at p < 0.1 were subjected to multivariate analysis and then the conventional US and clinicopathological characteristics independently associated with pathologic complete response (pCR) from the analysis were used to develop US models, clinicopathological models, and their combined models by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity to assess their predictive efficacy.ResultsThe combined model had an AUC of 0.808, a sensitivity of 88.72%, a specificity of 60.87%, and an accuracy of 75.81% in predicting pCR of HER2-positive breast cancer after NACT, which was significantly better than the clinicopathological model (AUC = 0.656) and the US model (AUC = 0.769). In addition, six characteristics were screened as independent predictors, namely the Clinical T stage, Clinical N stage, PR status, posterior acoustic, margin, and calcification.ConclusionThe conventional US combined with clinicopathological characteristics to construct a combined model has a good diagnostic effect in predicting pCR in HER2-positive breast cancer and is expected to be a useful tool to assist clinicians in effectively determining the efficacy of NACT in HER2-positive breast cancer patients.

Bilateral asymmetry of quantitative parenchymal kinetics at ultrafast DCE-MRI predict response to neoadjuvant chemotherapy in patients with HER2+ breast cancer

PurposeTo assess whether measurement of the bilateral asymmetry of semiquantitative and quantitative perfusion parameters from ultrafast dynamic contrast-enhanced MRI (DCE-MRI), allows early prediction of pathologic response after neoadjuvant chemotherapy (NAC) in patients with HER2+ breast cancer. Materials and methodsTwenty-eight female patients with HER2+ breast cancer treated with NAC who underwent pre-NAC ultrafast DCE-MRI (3–9 s/phase) were enrolled for this study. Four semiquantitative and two quantitative parenchymal parameters were calculated for each patient. Ipsilateral/contralateral (I/C) ratio (for four parameters) and the difference between (for two parameters) ipsi- and contra-lateral parenchymal kinetic parameters (kBPE) were compared for patients with pathologic complete response (pCR) and those having residual disease. Lasso regression with leave-one-out cross validation was used to determine the optimal combination of parameters for a regression model and multivariable logistic regression was used to identify independent predictors for pCR. Chi-squared test, two-sided t-test and Kruskal-Wallis test were used. ResultsThe Ktrans I/C ratio cutoff value of 1.11 had a sensitivity of 83.3% and specificity of 75% for pCR. The ve I/C ratio cutoff value of 1.1 had a sensitivity of 75% and specificity of 81.3% for pCR. The area under the receiver operating characteristic curve of the three-kBPE parameter model, including initial area under the enhancement curve (AUC30) I/C ratio, KtransI/C ratio and ve I/C ratio, was 0.89 with sensitivity of 91.7% at specificity of 81.3%. ConclusionQuantitative assessment of bilateral asymmetry kBPE from pre-NAC ultrafast DCE-MRI can predict pCR in patients with HER2+ breast cancer.

Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB-III non-small cell lung cancer patients using radiomic features.

To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non-small cell lung cancer (NSCLC). Patients with stage IB-III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features. Then, model 1 (five radiomics features before CIT), model 2 (four radiomics features after CIT and before surgery) and model 3 were constructed for the prediction of pCR. Model 3 included all nine features of model 1 and 2 and was later named the neoadjuvant chemoimmunotherapy-related pathological response prediction model (NACIP). This study included 110 patients: 77 in the training set and 33 in the validation set. Thirty-nine (35.5%) patients achieved a pCR. Model 1 showed area under the curve (AUC) = 0.65, 64% accuracy, 71% specificity, and 50% sensitivity, while model 2 displayed AUC = 0.81, 73% accuracy, 62% specificity, and 92% sensitivity. In comparison, NACIP yielded a good predictive value, with an AUC of 0.85, 81% accuracy, 81% specificity, and 83% sensitivity in the validation set. NACIP may be a potential model for the early prediction of pCR in patients with NSCLC treated with neoadjuvant/conversion CIT.

Imaging lymph node regression can predict the pathological tumor regression grade in gastric cancer after neoadjuvant treatment

BackgroundNeoadjuvant chemotherapy has become the standard treatment for locally advanced gastric cancer. The tumor regression grade system is an effective and widely used tool for the evaluation of treatment response to neoadjuvant chemotherapy. However, whether tumor regression grade could be predicted using clinical characteristics is uncertain. MethodsA total of 287 locally advanced gastric cancer patients from 2014 to 2021 were retrospectively included. According to the College of American Pathologists' tumor regression grade system, patients were classified into response group (tumor regression grade 0–1) and non-response group (tumor regression grade 2–3). Associations between clinical characteristics and neoadjuvant chemotherapy response were performed by the logistic regression model. The Kaplan–Meier method was used to estimate the survival. A prediction scoring system was constructed based on the β coefficients of multivariate analysis. The receiver operating characteristic curve and decision curve analysis were used to evaluate the performance of the predictive scoring system. ResultsSurvival analysis showed that patients with tumor regression grades 0 to 1 had significantly better disease-free survival and overall survival than the tumor regression grades 2 to 3. Tumor differentiation, ycT stage, immunotherapy, and lymph node regression were independent predictors of pathological response to neoadjuvant chemotherapy. We further developed a scoring system to predict the tumor regression grade. The receiver operating characteristic and decision curve analysis showed good predictive performance of the scoring system. ConclusionLymph node regression could be used as a predictor for pathological response. We developed a scoring system to predict the treatment response of patients with gastric cancer receiving neoadjuvant chemotherapy. The scoring system based on the predictors could provide guidance for making clinical decisions.

Ultrasound features combined with tumor-infiltrating lymphocytes for prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

Neoadjuvant chemotherapy (NAC) plays an important role in the treatment and prognosis of breast cancer. Theearly identification of patients who can truly benefit from preoperative NAC is crucial in clinical practice. The purpose of thisstudy was to explore whether the ultrasound features and clinical characteristics combined with tumor-infiltrating lymphocyte(TIL) levels can improve the performance of predicting NAC efficacy in breast cancer patients. Inthis retrospective study, 202 invasive breast cancer patients who underwent NAC followed by surgery were included. Thebaseline ultrasound features were reviewed by two radiologists. Miller-Payne Grading (MPG) was used to assess pathologicalresponse, and MPG 4-5 was defined as major histologic responders (MHR). Multivariable logistic regression analysis wasused to evaluate independent predictors for MHR and build the prediction models. The receiver operating characteristic (ROC)curve was used to evaluate the performance of the models. Of the 202 patients, 104 patients achieved MHR and 98patients achieved non-MHR. Multivariate logistic regression analysis showed the US size (p=0.042), molecular subtypes(p=0.001), TIL levels (p<0.001), shape (p=0.030), and posterior features (p=0.018) were independent predictors for MHR.The model combined the US features, clinical characteristics, and TIL levels had a better performance with an area under thecurve (AUC) of 0.811, a sensitivity of 0.663, and a specificity of 0.847. The model combined US features, clinicalcharacteristics, and TIL levels had a better performance in predicting pathological response to NAC in breast cancer.