The role of 18F-FDG PET/CT in predicting the pathological response to neoadjuvant PD-1 blockade in combination with chemotherapy for resectable esophageal squamous cell carcinoma

Dual energy CT-derived quantitative parameters and hematological characteristics predict pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell carcinoma patients

PURPOSE

There is no gold standard method to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients before surgery after neoadjuvant chemoradiotherapy (nCRT). This study aims to investigate whether dual layer detector dual energy CT (DECT) quantitative parameters and clinical features could predict pCR for ESCC patients after nCRT.

PATIENTS AND METHODS

This study retrospective recruited local advanced ESCC patients who underwent nCRT followed by surgical treatment from December 2019 to May 2023. According to pCR status (no visible cancer cells in primary cancer lesion and lymph nodes), patients were categorized into pCR group (N = 25) and non-pCR group (N = 28). DECT quantitative parameters were derived from conventional CT images, different monoenergetic (MonoE) images, virtual non-contrast (VNC) images, Z-effective (Zeff) images, iodine concentration (IC) images and electron density (ED) images. Slope of spectral curve (λHU), normalized iodine concentration (NIC), arterial enhancement fraction (AEF) and extracellular volume (ECV) were calculated. Difference tests and spearman correlation were used to select quantitative parameters for DECT model building. Multivariate logistic analysis was used to build clinical model, DECT model and combined model.

RESULTS

A total of 53 patients with locally advanced ESCC were enrolled in this study who received nCRT combined with surgery and underwent DECT examination before treatment. After spearman correlation analysis and multivariate logistic analysis, AEF and ECV showed significant roles between pCR and non-pCR groups. These two quantitative parameters were selected for DECT model. Multivariate logistic analysis revealed that LMR and RBC were also independent predictors in clinical model. The combined model showed the highest sensitivity, specificity, PPV and NPV compared to the clinical and DECT model. The AUC of the combined model is 0.893 (95%CI: 0.802-0.983). Delong's test revealed the combined model significantly different from clinical model (Z =-2.741, P = 0.006).

CONCLUSION

Dual-layer DECT derived ECV fraction and AEF are valuable predictors for pCR in ESCC patients after nCRT. The model combined DECT quantitative parameters and clinical features might be used as a non-invasive tool for individualized treatment decision of those ESCC patients. This study validates the role of DECT in pCR assessment for ESCC and a large external cohort is warranted to ensure the robustness of the proposed DECT evaluation criteria.

TP53-centric ctDNA complements PET/CT for non-invasive assessment of pathological complete response and survival after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a prospective cohort study

BACKGROUND

Accurate assessment of pathologic complete response (pCR) after neoadjuvant immunochemotherapy (NICT) is crucial to implement active surveillance or tailor therapeutic strategies for esophageal squamous cell carcinoma (ESCC), while reliable non-invasive methods for pCR prediction are lacking. We aimed to evaluate the potential of integrating circulating tumor DNA (ctDNA) and PET/CT for predicting pCR to NICT for ESCC.

METHODS

A total of 123 eligible patients were enrolled, including 68 patients from our prospective clinical trial (ChiCTR2000028900) and a real-world study (NCT04822103) that formed the discovery cohort, as well as 55 patients from another clinical trial (ChiCTR2100051763) comprising the validation cohort. Blood samples for ctDNA sequencing and PET/CT metrics were collected before and after NICT.

RESULTS

The ctDNA status and PET/CT parameters at the post-NICT stage rather than the pre-NICT stage significantly differentiated pCR from non-pCR patients. ctDNA and PET/CT synergistically enhanced the prediction of pCR from perspectives of sensitivity and specificity, respectively. The model integrating ctDNA concentration and mean standardized uptake value (SUVmean) demonstrated area under curves (AUCs) of 0.860 in the discovery cohort and 0.798 in the validation cohort for pCR prediction and stratified patients into high- and low-risk groups with differential survival prospects. The key gene modules converged on TP53 as the core mutation for pCR prediction, among which those located in the exon regions contributed the most to its predictive capacity. The model constructed based on TP53 mutation and SUVmean differentiated pCR from non-pCR with comparable performance to the model based on PET/CT and the overall ctDNA concentration.

CONCLUSION

The combination of post-treatment TP53 -centric ctDNA and PET/CT synergistically enhances the prediction of pCR following NICT in ESCC patients, indicating the potential to inform clinical decision-making for these patients.

Gastrointestinal tumor personalized immunotherapy: an integrated analysis from molecular genetics to imaging biomarkers

The immunotherapy landscape for gastrointestinal (GI) tumors is rapidly evolving. There is an urgent need for reliable biomarkers capable of predicting treatment outcomes to optimize therapeutic strategies and enhance patient prognosis. This review presents a comprehensive overview of biomarkers associated with the immunotherapy response of GI tumors, covering advances in molecular genetics, histopathological markers, and imaging. Key molecular biomarkers, such as microsatellite instability, tumor mutational burden, and programmed death-ligand 1 expression, remain critical for identifying patients likely to benefit from immune checkpoint inhibitors. The significance of tumor-infiltrating lymphocytes, notably the CD8+ T cell to regulatory T cell ratio, as a predictor of immunotherapy response is explored. In addition, advanced imaging techniques, including computed tomography (CT), magnetic resonance imaging, and positron emission tomography-CT, facilitate the noninvasive evaluation of tumor biology and therapeutic response. By bridging molecular and imaging data, this integrated strategy enhances precision in patient selection, treatment monitoring, and adaptive therapy design. Future studies should aim to validate these biomarkers in larger, multicenter cohorts and focus on clinical translation to advance precision medicine in GI oncology.

Preoperative assessment and prognostic prediction of gastric cancer patients with peritoneal metastasis using 18F-FDG PET/CT before conversion surgery

BACKGROUND

Conversion therapy followed by conversion surgery (CS) can improve the prognosis of gastric cancer (GC) patients with peritoneal metastasis (PM). However, patients benefit differently. There is no way to confirm the prognostic benefit non-invasively and early. This retrospective study assessed the value of 18F-FDG PET/CT after conversion therapy in preoperative assessment and prognostic prediction of GC patients with PM.

RESULTS

Fifty-one GC patients with PM were enrolled. 18F-FDG PET/CT after conversion therapy helped in preoperative assessment. Its diagnostic accuracy for residual peritoneal lesions was slightly better than contrast-enhanced CT (72.5% vs. 61.2%, P = 0.229), although the difference was not statistically significant. TBR of peritoneal lesions could help preoperative assessment, with TBR of peritoneal lesions to the mediastinal blood pool SUVmax (TBRAmaxp) as the best predictor (cutoff = 0.705, specificity 80%, sensitivity 80%, AUC 0.825, P < 0.001). Additionally, PET/CT could predict prognosis and assess surgical benefit. SUVmax of peritoneal lesions (SUVmaxp) was the best predictor of 24 months survival (cutoff = 1.466, AUC 0.870, P = 0.002, Specificity 77.8%, Sensitivity 83.3%) and metabolic parameters of peritoneal lesions could predict OS and the prognosis of patients who underwent CS.

CONCLUSION

18F-FDG PET/CT provides quantitative imaging indicators for preoperative evaluation and prognostic prediction in GC patients with PM.

Phase 2 trial of perioperative chemo-immunotherapy for gastro-esophageal adenocarcinoma: The role of M2 macrophage landscape in predicting response

We present the clinical results of a phase 2 trial combining neoadjuvant docetaxel, cisplatin, 5 Flourouracil, and the PD-L1 inhibitor avelumab in locally advanced gastro-esophageal adenocarcinoma (GEA). Fifty-one patients receive neoadjuvant therapy with 50 proceeding to surgery. Grade 3-4 adverse events occur in 40%; complete/major pathological response is found in 7/50 (14%) and 9/50 (18%), with 2-year disease-free survival of 67.5%. There is no correlation between tumor regression and PD-L1 or mismatch repair (MMR) status. Multiplex immunohistochemistry and longitudinal single-cell transcriptomic profiling reveal alterations in certain innate immune cell populations, particularly noting an M2-tumor-associated macrophage (M2-TAM) proliferation in non-responding tumors. These findings describe the effective nature of this treatment regimen for GEA and reveal associated features of the inflammatory milieux associated with response to chemo-immunotherapy. The specific character of the inflammatory environment in non-responders may, in the future, help personalize treatment. This study was registered at ClinicalTrials.gov (NCT03288350).

[18F]FDG PET/CT for predicting neoadjuvant PD-L1 blockade monotherapy treatment response in patients with locally advanced esophageal squamous cell carcinoma: a preliminary study

PURPOSE

To investigate the predictive value of 2-[18F]-fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT for evaluating primary tumor (PT) and lymph node (LN) responses after neoadjuvant programmed death-ligand 1 (PD-L1) blockade monotherapy in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC).

METHODS

In the single-arm phase 1b NATION-1907 trial (NCT04215471), 23 patients with LA-ESCC received two cycles of neoadjuvant PD-L1 blockade Adebrelimab followed by surgery. Among these, 18 patients underwent [18F]FDG PET/CT scans both before immunotherapy and prior to surgery. Standardized uptake value corrected for lean body mass (SUL)-derived parameters, including SULmax and SULpeak, were documented for PTs and LNs. Lesions > 1cm3 were segmented using thresholds of 41% and 50% of SULmax, respectively, following European Association of Nuclear Medicine (EANM) guidelines, with metabolic tumor volume (MTV) and total lesion glycolysis (TLG) calculated. Percentage changes of all metabolic parameters were also recorded. Residual viable tumor ≤ 33% were classified as well-responders, whereas residual viable tumor > 33% were classified as poor-responders based on histological evaluation.

RESULTS

In the PT analysis, 10 patients were classified as PT well-responders and 8 as PT poor-responders. All post-treatment metabolic parameters, except MTV, were significantly lower in well-responders compared to poor-responders. The %ΔMTV, %ΔTLG were significantly higher in the poor-responder group (all P < 0.05). ROC curves indicated %ΔMTV41 exhibited optimum performance in predicting well-responders, with an AUC of 0.875 (cut-off: -31.01). Furthermore, %ΔMTV41 significantly predicted patients' recurrence-free survival (RFS) (P < 0.1). In the LN analysis, 7 LNs were classified as well-responders and 10 as poor-responders. Pre-treatment SULmax, SULpeak were significantly lower in poor-responders compared to well-responders. Post-treatment MTV50 and all percentage changes in parameters were significantly higher in the poor-responder group (all P < 0.05). Receiver operating characteristic curve (ROC) analysis indicated %ΔTLG50 had excellent predictive performance for well-responders, with an AUC of 1.000 (cut-off: -7.5). However, there was no significant correlation between the metabolic response evaluations for PTs and LNs.

CONCLUSION

The metabolic parameters of [18F]FDG PET/CT, particularly %ΔMTV and %ΔTLG, could effectively predict well-responders among both PTs and LNs to neoadjuvant PD-L1 blockade monotherapy in LA-ESCC, which may facilitate personalized immunotherapy and serve as a stratification tool in future larger-scale studies.

Prediction of pathological response to neoadjuvant immunochemotherapy with baseline and post-treatment 18F-FDG PET imaging biomarkers in patients with locally advanced gastric cancer

BACKGROUND

Neoadjuvant immunochemotherapy (NICT) has shown promising therapeutic benefits in patients with locally advanced gastric cancer (LAGC). Our study aimed to predict the pathological response to NICT in LAGC before surgery by correlating the metabolic parameters of baseline and post-treatment 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) of the primary lesion with the pathological response following radical surgery.

METHODS

Thirty-six LAGC patients who received three cycles of NICT (combination of sintilimab and CapeOx), followed by radical surgery, were included in this study. Both baseline 18F-FDG PET/CT (bPET) and post-treatment 18F-FDG PET/CT (pPET) were conducted, the metabolic parameters derived from the PET/CT scans, including the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on bPET and pPET (bSUVmax and pSUVmax, bMTV and pMTV, bTLG and pTLG), as well as their reductions post-treatment (ΔSUVmax, ΔMTV, and ΔTLG), were assessed for their correlation with treatment efficacy and tumor regression grade (TRG) following NICT.

RESULTS

Out of the 36 patients, 13 patients had a good response (GR), which included 5 cases with TRG 0 and 8 cases with TRG 1. Conversely, 23 patients exhibited a poor response (PR), with 20 patients having TRG 2 and 3 patients having TRG 3. Univariate analysis revealed that pMTV and pTLG in the GR group were significantly lower compared to the PR group (all p < 0.05). The identified cutoff values of pMTV and pTLG were 1.68 cm³ (area under the cure (AUC) = 0.683) and 4.71 cm³ (AUC = 0.683) for the GR and PR groups, respectively. On receiver operating characteristic (ROC) curve analyses, these values corresponded to sensitivity, specificity, and accuracy of 68.8%, 80.0%, and 73.1%, respectively, with no statistically significant differences between them after the DeLong test and McNemar test (all p > 0.05). Furthermore, bSUVmax, bMTV, bTLG, ΔSUVmax, ΔMTV, and ΔTLG in the TRG 0 group were significantly higher than those in the TRG 1 group (all p < 0.05). Upon performing ROC curve analyses for the TRG 0 group, the thresholds for bSUVmax, bMTV, bTLG, ΔSUVmax, ΔMTV, and ΔTLG were determined to be 7.8 (AUC = 0.916), 36.76 (AUC = 0.768), 105.55 (AUC = 0.819), 4.82 (AUC = 0.923), 22.64 (AUC = 0.807), and 104.7 (AUC = 0.845), with no statistically significant differences between them after the DeLong test (all p > 0.05). These thresholds demonstrated high sensitivity (80% for bMTV and 100% for others), specificity (83.9%, 71.0%, 67.7%, 83.9%, 61.3%, and 71.0%), and accuracy (86.1%, 66.7%, 72.2%, 86.1%, 66.7%, and 75.0%) in predicting TRG 0 after NICT, with no statistically significant differences between them after the McNemar test (all p > 0.05).

CONCLUSIONS

Imaging biomarkers from the combination of baseline and post-treatment 18F-FDG PET/CT showed potential in predicting pathological response to NICT in LAGC patients before surgery.

The Potential of Basal F-18-FDG PET/CT in Evaluating Prognosis and Benefit From Adjuvant Chemotherapy After Tumor Resection of Stage IB(T2, ≤ 3 cm With VPI, N0, M0)NSCLC

OBJECTIVES

To investigated whether the basal F-18-FDG PET/CT could evaluate the prognosis or the benefit from adjuvant chemotherapy after surgery of patients with early-stage NSCLC with visceral pleural invasion.

MATERIALS AND METHODS

A total of 116 patients with stage IB (T2, ≤ 3 cm with VPI, N0, M0) NSCLC underwent tumor resection and F-18-FDG PET/CT 1-3 weeks before surgery and were followed up for 1-79 months after surgery. SUVpeak, SUVmax, SUVmean, MTV, and TLG of tumors were obtained. The primary and secondary endpoints were progression-free survival (PFS) and overall survival (OS), respectively. ROC curve analysis, Cox regression test, and the Kaplan-Meier method were used for statistical analysis.

RESULTS

High SUVs, TLG, and MTV were associated with postoperative progression of NSCLC (the area under the ROC curve: 0.695 to 0.750, P < .001). The increase of SUVs, TLG or MTV was associated with short postoperative PFS (P < .001) while an increase in TLG (P = .016) or MTV (P = .018) was associated with short postoperative OS. TLG > 16.81 was an independent indicator of both the short PFS (HR = 5.534, P = .002) and the short OS (HR = 5.075, P = .031). Further, adjuvant chemotherapy was associated with longer PFS in NSCLCs with TLG > 16.81 (treated vs. untreated: 63 vs. 52 months; HR = 2.242, P = .022) rather than those with TLG ≤ 16.81.

CONCLUSION

SUV-based parameters on F-18-FDG PET/CT have the potential to evaluate the prognosis and benefit from adjuvant chemotherapy after tumor resection in stage IB (T2, ≤ 3 cm with VPI, N0, M0) NSCLC and therefore may be helpful for lung cancer treatment.

CT-based delta-radiomics for predicting pathological response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a multicenter study

BACKGROUND

The study aimed to investigate the predictive value of delta-radiomics derived from computed tomography (CT) for pathological complete response (pCR) to neoadjuvant immunochemotherapy (NICT) among patients with esophageal squamous cell carcinoma (ESCC), helping clinicians determine whether to modify the neoadjuvant treatment strategy, proceed to surgery, or forgo surgery altogether.

METHODS

A total of 140 ESCC patients from two institutions (Database 1 = 93; Database 2 = 47) who underwent NICT and surgery were retrospectively included in the study. The training set consisted of patients from Database 1, while the testing set included patients from Database 2. All patients underwent contrast-enhanced CT scans before the start of the treatment and prior to the operation. The delta-radiomics features were calculated as the relative net change of radiomics features between the two-time points. Feature selection was conducted using Pearson correlation analysis, intraclass correlation coefficients, and the fivefold cross-validation with least absolute shrinkage and selection analysis. Four models were established, comprising a clinical model, a pre-treatment radiomics model, a delta-radiomics model, and a mixed model. Area under the curve (AUC) and decision curve analysis were used to assess the performance and the clinical value of the models.

RESULTS

Less than half of the tumors (40/140, 28.6%) showed pCR following NICT. The delta-radiomics model displayed AUC of 0.827 and 0.790 in the training and testing set for predicting pCR, which was superior to the clinical model based on age and clinical tumor node metastasis (cTNM) stage (0.758 and 0.615) and the pre-treatment radiomics model (0.787 and 0.621). Furthermore, the delta-radiomics model demonstrated a more excellent AUC value in the testing set than the mixed model (0.847 and 0.719), which integrated clinical and delta-radiomics features.

CONCLUSIONS

The delta-radiomics model exhibited better diagnostic performance in preoperative prediction of pCR for NICT in ESCC patients compared to the clinical, pre-treatment radiomics, and mixed models.

PYGB targeted by androgen receptor contributes to tumor progression and metabolic reprogramming in esophageal squamous carcinoma

BACKGROUND

The incidence and mortality rates of esophageal squamous cell carcinoma (ESCC) are conspicuously augmented in men in contrast to women. The androgen receptor (AR), prevalently associated with the manifestation of male characteristics, is regarded as a pivotal determinant in tumor progression. Nevertheless, its exact role in ESCC remains insufficiently delineated.

METHODS

In this study, we probed the expression levels of AR and glucose metabolism enzymes in ESCC tissues by means of immunohistochemistry. We exploited chromatin immunoprecipitation and dual luciferase reporter assays to delve into the transcriptional regulatory interrelationships between AR and these enzymes. A gamut of molecular techniques-including multi-omics sequencing, colony formation assays, cell counting kit 8 (CCK8), 5-Ethynyl-2'-deoxyuridine (EdU) incorporation assays, wound-healing assays, transwell migration assays, extracellular acidification rate (ECAR) measurements, lipid droplet fluorescence imaging, and xenograft models-were enlisted to illuminate the functions of these enzymes within ESCC cells.

RESULTS

Our discoveries manifested that AR expression was strikingly higher in male ESCC tissues than in their female counterparts. Significantly, we discerned that glycogen phosphorylase B (PYGB), a cardinal enzyme implicated in glucose metabolism, demonstrated not only a positive correlation with AR expression but also an association with adverse prognostic outcomes for ESCC patients. Moreover, AR directly binds to the promoter region of the PYGB gene, thereby potentiating its transcriptional activity. This upregulation of PYGB was ascertained to facilitate proliferation, invasion, and metastasis among ESCC cells while intensifying glycolysis and modifying lipid metabolism pathways within these cells. In animal models employing nude mice, elevated PYGB levels were witnessed to expedite subcutaneous tumor growth as well as lung metastasis.

CONCLUSIONS

Collectively, our study establishes PYGB as a direct target of AR that assumes an indispensable role in both tumor progression and metabolic reprogramming affiliated with ESCC, thus paving novel avenues for therapeutic strategies centered on metabolic intercessions.

18F-Fluoride PET/CT-Updates

Sodium Fluoride-18 production started in the 1940s and was described clinically for the first time in 1962 as a bone-imaging agent. However, its use became dormant with the development of conventional bone scintigraphy, especially due to its low cost. Conventional bone scintigraphy has been the most utilized Nuclear Medicine technique for identifying osteoblastic bone metastases, especially in prostate and breast cancers for decades and is also employed to identify benign bone disease, especially in the orthopedic setting. While bone scintigraphy is highly sensitive, it lacks adequate specificity. With the advent of high-quality 3D Whole-Body Positron Emission Tomography combined with computed tomography (PET/CT), images, Sodium Fluoride-18 imaging with PET/CT (Fluoride PET/CT) re-emerged. This PET/CT bone-imaging agent provides higher sensitivity and specificity to detect bone lesions in both the oncological scenario as well as to identify benign bone and joint disorders. PET/CT bone-imaging provides a precise view of the bone metabolism remodeling processes at a molecular level, throughout the skeleton, and combines anatomical information, enhancing diagnostic specificity and accuracy. This article review will explore the updates on clinical applications of Fluoride PET/CT in oncology and benign conditions encompassing orthopedic, inflammatory and cardiovascular conditions and treatment response assessment.

Preoperative dual-energy computed tomography and positron-emission tomography evaluation of lymph node metastasis in esophageal squamous cell carcinoma

PURPOSE

To investigate the detectability of lymph node metastasis in patients with esophageal squamous cell carcinoma using a combination of dual-energy computed tomography (CT) and positron-emission tomography (PET) parameters.

METHODS

We analyzed dual-energy CT and PET preoperative data in 27 consecutive patients with esophageal squamous cell carcinoma (23 men, 4 women; mean age, 73.7 years). We selected lymph nodes with a short-axis diameter of ≥5 mm and measured CT values, iodine concentrations, fat fractions, long- and short-axis diameters, and ratio of long- and short-axis diameters. We performed visual assessment of lymph node characteristics based on dual-energy CT and determined the maximum standardized uptake value via PET. The measured values were postoperatively compared between pathologically confirmed metastatic and nonmetastatic lymph nodes. Stepwise logistic regression analysis was performed to determine factors associated with lymph node metastasis. Diagnostic accuracy was assessed via receiver operating characteristic curve analysis.

RESULTS

Overall, 18 metastatic and 37 nonmetastatic lymph nodes were detected. CT values, iodine concentrations, fat fractions, and the maximum standardized uptake values differed significantly between metastatic and nonmetastatic lymph nodes (p < 0.05). Stepwise logistic regression showed that iodine concentration and the maximum standardized uptake value were significant predictors of metastatic lymph nodes. The areas under the curve of iodine concentrations and maximum standardized uptake values were 0.809 and 0.833, respectively. The area under the curve of the combined parameters was 0.884, with 83.3% sensitivity and 86.5% specificity.

CONCLUSION

Combined dual-energy CT and PET parameters improved the diagnosis of lymph node metastasis in patients with esophageal cancer.

Prognostic significance of PET/CT and its association with immuno-genomic profiling in oesophageal squamous cell carcinoma treated with immunotherapy plus chemoradiotherapy: results from a phase II study

BACKGROUND

A phase II trial (EC-CRT-001) demonstrated the promising efficacy of combining toripalimab (an anti-PD-1 antibody) with definitive chemoradiotherapy (CRT) for locally advanced oesophageal squamous cell carcinoma (ESCC). Biomarkers are key to identifying patients who may benefit from this therapeutic approach.

METHODS

Of the 42 patients with ESCC who received toripalimab combined with definitive CRT, 37 were included in this analysis. Baseline assessments included PET/CT metabolic parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG), RNA sequencing of tumour biopsies to quantify the tissue mutational burden (TMB), and multiplex immunofluorescence staining to estimate immune cell infiltration in the tumour microenvironment (TME). Frozen neoplastic samples were procured for RNA sequencing to further explore the immune-related TME.

RESULTS

Among the 37 patients, high baseline SUVmax (≥12.0; OR = 6.5, 95% CI 1.4-48.2, p = 0.032) and TLG (≥121.8; OR = 6.8, 95% CI 1.6-33.5, p = 0.012) were significantly correlated with lower complete response rates. All five PET/CT parameters were notably associated with overall survival; only SUVmax and TLG were associated with a significantly worse progression-free survival. A trend towards an inverse correlation was observed between SUVmax and TMB (R = -0.33, p = 0.062). PD-1 + CD8 + T cell infiltration was negatively correlated with MTV (R = -0.355, p = 0.034) and TLG (R = -0.385, p = 0.021). Moreover, RNA sequencing revealed that the high TLG subgroup exhibited low immune cell infiltration, indicating an immunosuppressive landscape.

CONCLUSIONS

High baseline SUVmax and TLG might predict poorer treatment response and worse survival in patients with ESCC undergoing immunotherapy combined with CRT. In addition, high PET/CT metabolic parameters, particularly TLG, were correlated with an immunosuppressive TME, which warrants further exploration.

Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study

PURPOSE

To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning.

METHODS AND MATERIALS

In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis.

RESULTS

The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed.

CONCLUSIONS

Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer

Purpose

This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC).

Methods

A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance.

Results

The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application.

Conclusions

The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.

Early Contrast-Enhanced MR for Diagnosing Complete Tumor Response of Locally Advanced Esophageal Squamous Cell Carcinoma After Neoadjuvant Therapy: A Retrospective Comparative Study

BACKGROUND

This study assessed the performance of early contrast-enhanced magnetic resonance (ECE-MR) in the detecting of complete tumor response (ypT0) in patients with esophageal squamous cell carcinoma following neoadjuvant therapy.

PATIENTS AND METHODS

Preoperative MR images of consecutive patients who underwent neoadjuvant therapy and surgical resection were reviewed retrospectively. The accuracy of ECE-MR and T2WI+DWI was evaluated by comparing the findings with pathological results. Receiver operating characteristic curve analysis was used to assess the diagnostic performance, and DeLong method was applied to compare the areas under the curves (AUC). Chi-squared analysis was conducted to explore the difference in pathological changes.

RESULTS

A total of 198 patients (mean age 62.6 ± 7.8 years, 166 men) with 201 lesions were included. The AUC of ECE-MR was 0.85 (95% CI 0.79-0.90) for diagnosing ypT1-4, which was significantly higher than that of T2WI+DWI (AUC 0.69, 95% CI 0.63-0.76, p < 0.001). The diagnostic performance of both T2WI+DWI and ECE-MR improved with increasing tumor stage. The AUCs of ECE-MRI were higher in ypT1 and ypT2 tumors than T2WI+DWI. Degree 2-3 tumor-infiltrating lymphocytes and neutrophils were commonly seen in ypT0 tumors misdiagnosed by ECE-MR.

CONCLUSIONS

Visual evaluation of ECE-MR is a promising diagnostic protocol for the detection of complete tumor response, especially for differentiation with early stage tumors. The accurate diagnosis of complete tumor response after neoadjuvant therapy using imaging modalities is of important significance for clinical decision-making for patients with esophageal squamous cell carcinoma. It is hoped that early contrast-enhanced MR will provide supportive advice for the development of individualized treatment options for patients.

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

BACKGROUND

Neoadjuvant immunochemotherapy (NICT) plus esophagectomy has emerged as a promising treatment option for locally advanced esophageal squamous cell carcinoma (LA-ESCC). Pathologic complete response (pCR) is a key indicator associated with great efficacy and overall survival (OS). However, there are insufficient indicators for the reliable assessment of pCR.

METHODS

192 patients with LA-ESCC treated with NICT from December 2019 to October 2023 were recruited. According to pCR status, patients were categorized into pCR group (22.92%) and non-pCR group (77.08%). Radiological features of pretreatment and preoperative CT images were extracted. Logistic and COX regressions were trained to predict pathological response and prognosis, respectively.

RESULTS

Four of the selected radiological features were combined to construct an ESCC preoperative imaging score (ECPI-Score). Logistic models revealed independent associations of ECPI-Score and vascular sign with pCR, with AUC of 0.918 in the training set and 0.862 in the validation set, respectively. After grouping by ECPI-Score, a higher proportion of pCR was observed among the high-ECPI group and negative vascular sign. Kaplan Meier analysis demonstrated that recurrence-free survival (RFS) with negative vascular sign was significantly better than those with positive (P = 0.038), but not for OS (P = 0.310).

CONCLUSIONS

This study demonstrates dynamic radiological features are independent predictors of pCR for LA-ESCC treated with NICT. It will guide clinicians to make accurate treatment plans.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Background

Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.

Methods

A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.

Results

The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.

Conclusion

This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

Preoperative prediction of pancreatic neuroendocrine tumor grade based on 68Ga-DOTATATE PET/CT

OBJECTIVE

To establish a prediction model for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs) based on 68Ga-DOTATATE PET/CT.

METHODS

Clinical data of 41 patients with PNETs were included in this study. According to the pathological results, they were divided into grade 1 and grade 2/3. 68Ga-DOTATATE PET/CT images were collected within one month before surgery. The clinical risk factors and significant radiological features were filtered, and a clinical predictive model based on these clinical and radiological features was established. 3D slicer was used to extracted 107 radiomic features from the region of interest (ROI) of 68Ga-dotata PET/CT images. The Pearson correlation coefficient (PCC), recursive feature elimination (REF) based five-fold cross validation were adopted for the radiomic feature selection, and a radiomic score was computed subsequently. The comprehensive model combining the clinical risk factors and the rad-score was established as well as the nomogram. The performance of above clinical model and comprehensive model were evaluated and compared.

RESULTS

Adjacent organ invasion, N staging, and M staging were the risk factors for PNET grading (p < 0.05). 12 optimal radiomic features (3 PET radiomic features, 9 CT radiomic features) were screen out. The clinical predictive model achieved an area under the curve (AUC) of 0.785. The comprehensive model has better predictive performance (AUC = 0.953).

CONCLUSION

We proposed a comprehensive nomogram model based on 68Ga-DOTATATE PET/CT to predict grade 1 and grade 2/3 of PNETs and assist personalized clinical diagnosis and treatment plans for patients with PNETs.

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.

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.

18 F-FDG PET/CT predicts the role of neoadjuvant immunochemotherapy in the pathological response of esophageal squamous cell carcinoma

BACKGROUND

This study aimed to investigate the predictive value of 18 F-FDG PET/CT for pathological response after neoadjuvant immunochemotherapy (NICT) in patients with esophageal squamous cell carcinoma (ESCC).

METHODS

The clinical data of 54 patients with ESCC who underwent two cycles of NICT followed by surgery were retrospectively analyzed. NICT consisted of PD-1 blockade therapy combined with chemotherapy. 18 F-FDG PET/CT scans were performed before and after NICT. The pathological results after surgery were used to assess the degree of pathological response. The scan parameters of 18 F-FDG PET/CT and their changes before and after NICT were compared with the pathological response.

RESULTS

Among the 54 patients, 10 (18.5%) achieved complete pathological response (pCR) and 21 (38.9%) achieved major pathological response (MPR). The post-NICT scan parameters and their changes were significantly associated with the pathological response. In addition, the values of the changes in the scanned parameters before and after treatment can further predict the pathological response of the patient.

CONCLUSION

18 F-FDG PET/CT is a useful tool to evaluate the efficacy of NICT and predict pathological response in patients with ESCC. The post-NICT scan parameters and their changes can help identify patients who are likely to achieve pCR or MPR.

Evaluation of neoadjuvant immunotherapy and traditional neoadjuvant therapy for resectable esophageal cancer: a systematic review and single-arm and network meta-analysis

Objective

This systematic review and meta-analysis aimed to investigate the role of neoadjuvant immunochemotherapy with or without radiotherapy [NIC(R)T] compared to traditional neoadjuvant therapies, without immunotherapy [NC(R)T].

Summary background data

NCRT followed by surgical resection is recommended for patients with early-stage esophageal cancer. However, it is uncertain whether adding immunotherapy to preoperative neoadjuvant therapy would improve patient outcomes when radical surgery is performed following neoadjuvant therapy.

Methods

We searched PubMed, Web of Science, Embase, and Cochrane Central databases, as well as international conference abstracts. Outcomes included R0, pathological complete response (pCR), major pathological response (mPR), overall survival (OS) and disease-free survival (DFS) rates.

Results

We included data from 5,034 patients from 86 studies published between 2019 and 2022. We found no significant differences between NICRT and NCRT in pCR or mPR rates. Both were better than NICT, with NCT showing the lowest response rate. Neoadjuvant immunotherapy has a significant advantage over traditional neoadjuvant therapy in terms of 1-year OS and DFS, with NICT having better outcomes than any of the other three treatments. There were no significant differences among the four neoadjuvant treatments in terms of R0 rates.

Conclusions

Among the four neoadjuvant treatment modalities, NICRT and NCRT had the highest pCR and mPR rates. There were no significant differences in the R0 rates among the four treatments. Adding immunotherapy to neoadjuvant therapy improved 1-year OS and DFS, with NICT having the highest rates compared to the other three modalities.

Systematic Review Registration

https://inplasy.com/inplasy-2022-12-0060/, identifier INPLASY2022120060.

Residual tumor model in esophageal squamous cell carcinoma after neoadjuvant immunochemotherapy: Frequently involves the mucosa and/or submucosa

Objectives

The efficacy and safety of neoadjuvant immunochemotherapy (nICT) are widely explored in locally advanced esophageal squamous cell carcinoma (ESCC). Whether the "wait-and-see" strategy is applicable in ESCC after nICT is still lacking a theoretical basis. This study aimed to preliminarily explore the distribution of residual tumors and the regression pattern of ESCC after nICT.

Methods

Patients undergoing radical esophagectomy after nICT in Fujian Medical University Union Hospital between January 2020 and March 2022 were identified. The resection specimens were re-evaluated by one experienced pathologist. The pathological response was assessed by tumor regression grade (TRG) (modified Ryan scheme). The TRG grade was divided into grades 0 (pathological complete response), 1, 2, and 3. The pathological stage was evaluated in the Eighth Edition AJCC. In the non-pCR group, the residual model was divided into four types: Type I, regression towards the lumen; type II, regression towards the invasive front; type III, concentric regression; and type IV, scattered regression.

Results

A total of 95 consecutive patients were included for analysis. Seventy-six (80.0%) of 95 patients were in non-pCR (pathological complete response), and nine patients (9/76, 11.84%) had isolated residual tumors in lymph nodes. There was no significant difference in baseline characteristics between the pCR group and the non-pCR group (p > 0.05). The overall distribution of TRG for all esophageal wall layers was TRG 0 = 28 (28/95, 29.5%), TRG 1 = 17 (17/95, 17.9%), TRG 2 = 18 (18.9%, 18/95), and TRG 3 = 32 (32/95, 33.7%). In 67 patients with residual tumors in the esophageal wall (TRG ≧1), 63 (63/67, 94.0%) had residual tumor cells in the mucosa and/or submucosa, and four had isolated residual tumors in the muscle layer (4/67, 6.0%). Further analysis showed eight (8/67, 11.9%) patients with submucosal involvement but without mucosal involvement. The distribution of regression patterns was type I (n = 35, 52.2%), type II (n = 3, 4.5%), type III (n = 8, 11.9%), and type IV (n = 21, 31.3%).

Conclusions

The mucosa and/or submucosa are frequently involved in residual malignancy, and the frequent regression models are regression toward the lumen and random regression. There is an opportunity to carefully test the residual tumors in a subgroup of the population with ESCC following nICT. However, some patients had residual tumors only in the muscle layer or lymph nodes. The clinical application of the wait-and-see strategy in ESCC after nICT should be explored using an appropriate evaluation protocol.