Neoadjuvant SHR-1701 with or without chemotherapy in unresectable stage III non-small-cell lung cancer: A proof-of-concept, phase 2 trial.

We conducted a proof-of-concept, phase 2 trial to assess neoadjuvant SHR-1701 with or without chemotherapy, followed by surgery or radiotherapy, and then consolidation SHR-1701 in unresectable stage III non-small-cell lung cancer (NSCLC). In the primary cohort of patients receiving neoadjuvant combination therapy (n = 97), both primary endpoints were met, with a post-induction objective response rate of 58% (95% confidence interval [CI] 47-68) and an 18-month event-free survival (EFS) rate of 56.6% (95% CI 45.2-66.5). Overall, 27 (25%) patients underwent surgery; all achieved R0 resection. Among them, 12 (44%) major pathological responses and seven (26%) pathological complete responses were recorded. The 18-month EFS rate was 74.1% (95% CI 53.2-86.7) in surgical patients and 57.3% (43.0-69.3) in radiotherapy-treated patients. Neoadjuvant SHR-1701 with chemotherapy, followed by surgery or radiotherapy, showed promising efficacy with a tolerable safety profile in unresectable stage III NSCLC. Surgical conversion was feasible in a notable proportion of patients and associated with better survival outcomes.

A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.

BACKGROUND: To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model. METHODS: We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomogram were assessed utilizing calibration curve and the area under the receiver operating factor curve (AUC), and the clinical benefits were assessed utilizing decision curve analysis (DCA). RESULTS: We constructed three predictive models.The AUC values of the radiomics signature and hematology model reached 0.874 (95% CI: 0.819-0.928) and 0.772 (95% CI: 0.699-0.845), respectively. Tumor length, cN stage, the radScore, and the hemScore were found to be independent factors influencing pCR according to univariate and multivariate analyses (P < 0.05). A combined nomogram was constructed from these factors, and AUC reached 0.934 (95% CI: 0.896-0.972). DCA demonstrated that the clinical benefits brought by the nomogram for patients across an extensive range were greater than those of other individual models. CONCLUSIONS: By combining CT radiomics, hematological factors, and clinicopathological characteristics before treatment, we developed a nomogram model that effectively predicted whether ESCC patients would achieve pCR after nICT, thus identifying patients who are sensitive to nICT and assisting in clinical treatment decision-making.

Accurate location describe and management of lymph node recurrence after esophagectomy for thoracic esophageal squamous cell carcinoma: a retrospective cohort study.

BACKGROUND: Describe the accurate locations of lymph node recurrence (LNR) of Chinese patients with postoperative thoracic esophageal squamous cell carcinoma (ESCC) is essential for determining the need for further surveillance protocols and treatments. The authors aimed to evaluate the patterns of postoperative ESCC and its current risk stratification with LNR. METHODS: This population-based cohort study included a retrospective review of the medical records and image material of patients with ESCC who underwent LNR after radical surgery between January 2013 and September 2022, with a median follow-up time of 5.71 years. Clinical features were extracted from these records, and survival analysis was performed. The primary endpoint was the accurate location and range of LNR according to the nomenclature of the Japanese Society for Esophageal Diseases. The second endpoints was to explore the related factors of recurrence range and overall survival. RESULTS: A total of 3268 lymph node regions were recurrence from 1129 patients, with a mean of 2.89 regions per patient. No.104, 106, and 107 was the most common recurrence of thoracic ESCC with an LNR rate higher than 15%. In upper thoracic ESCC, No.105 was a common recurrence site and abdominal LNR was rare. In lower thoracic ESCC, retroperitoneal lymph node was a unique regions (15.4%). Anastomotic recurrence is an important recurrence pattern in patients with postoperative esophageal cancer, with an incidence of 24.5%. Rates of LNR in range of lymph node dissection was low (13.9%). The median time of LRT was 20.0 (1.5-184.0) months. High range of recurrence was associated with significantly poorer OS in patients. Multiple linear regression analysis identified demonstrated N stage, tumor differentiation, adjuvant radiotherapy, and total lymph nodes removed were association with recurrence range for patients. CONCLUSIONS: Supraclavicular and upper mediastinums lymph nodes were common recurrence site for ESCC patients, and careful initial staging and surveillance are needed. Thorough lymph node dissection may reduce the range of regional recurrence.

Synthesis of virtual monoenergetic images from kilovoltage peak images using wavelet loss enhanced CycleGAN for improving radiomics features reproducibility.

Background: Dual-energy computed tomography (CT) can provide a range of image information beyond conventional CT through virtual monoenergetic images (VMIs). The purpose of this study was to investigate the impact of material decomposition in detector-based spectral CT on radiomics features and effectiveness of using deep learning-based image synthesis to improve the reproducibility of radiomics features. Methods: In this paper, spectral CT image data from 45 esophageal cancer patients were collected for investigation retrospectively. First, we computed the correlation coefficient of radiomics features between conventional kilovoltage peak (kVp) CT images and VMI. Then, a wavelet loss-enhanced CycleGAN (WLL-CycleGAN) with paired loss terms was developed to synthesize virtual monoenergetic CT images from the corresponding conventional single-energy CT (SECT) images for improving radiomics reproducibility. Finally, the radiomic features in 6 different categories, including gray-level co-occurrence matrix (GLCM), gray-level difference matrix (GLDM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and wavelet, were extracted from the gross tumor volumes from conventional single energy CT, synthetic virtual monoenergetic CT images, and virtual monoenergetic CT images. Comparison between errors in the VMI and synthetic VMI (sVMI) suggested that the performance of our proposed deep learning method improved the radiomic feature accuracy. Results: Material decomposition of dual-layer dual-energy CT (DECT) can substantially influence the reproducibility of the radiomic features, and the degree of impact is feature dependent. The average reduction of radiomics errors for 15 patients in testing sets was 96.9% for first-order, 12.1% for GLCM, 12.9% for GLDM, 15.7% for GLRLM, 50.3% for GLSZM, 53.4% for NGTDM, and 6% for wavelet features. Conclusions: The work revealed that material decomposition has a significant effect on the radiomic feature values. The deep learning-based method reduced the influence of material decomposition in VMIs and might improve the robustness and reproducibility of radiomic features in esophageal cancer. Quantitative results demonstrated that our proposed wavelet loss-enhanced paired CycleGAN outperforms the original CycleGAN.

CT synthesis from MR images using frequency attention conditional generative adversarial network.

Magnetic resonance (MR) image-guided radiotherapy is widely used in the treatment planning of malignant tumors, and MR-only radiotherapy, a representative of this technique, requires synthetic computed tomography (sCT) images for effective radiotherapy planning. Convolutional neural networks (CNN) have shown remarkable performance in generating sCT images. However, CNN-based models tend to synthesize more low-frequency components and the pixel-wise loss function usually used to optimize the model can result in blurred images. To address these problems, a frequency attention conditional generative adversarial network (FACGAN) is proposed in this paper. Specifically, a frequency cycle generative model (FCGM) is designed to enhance the inter-mapping between MR and CT and extract more rich tissue structure information. Additionally, a residual frequency channel attention (RFCA) module is proposed and incorporated into the generator to enhance its ability in perceiving the high-frequency image features. Finally, high-frequency loss (HFL) and cycle consistency high-frequency loss (CHFL) are added to the objective function to optimize the model training. The effectiveness of the proposed model is validated on pelvic and brain datasets and compared with state-of-the-art deep learning models. The results show that FACGAN produces higher-quality sCT images while retaining clearer and richer high-frequency texture information.

Assessing the Impact of a 1.5†T Transverse Magnetic Field in Radiotherapy for Esophageal Cancer Patients.

Purpose: Magnetic resonance (MR)-guided radiotherapy enables visualization of static anatomy, capturing tumor motion, and extracting quantitative image features for treatment verification and outcome monitoring. However, magnetic fields in online MR imaging (MRI) require efforts to ensure accurate dose measurements. This study aimed to assess the dosimetric impact of a 1.5 T magnetic field in esophageal cancer radiotherapy using MR-linac, exploring treatment adaptation potential and personalized medicine benefits. Methods: A prospective cohort study enrolled 100 esophageal squamous cell carcinoma patients undergoing 4DCT and 3DCT scans before radiotherapy. The heart was contoured on 3DCT, 4DCT end expiration (EE), and 4DCT end inhalation (EI) images by the same radiation oncologist. Reference RT plans were designed on 3DCT, with adjustments for different phases generating 5 plan types per patient. Variations in dose-volume parameters for organs at risk and the target area among different plans were compared using Monaco 5.40.04. Results: Slight dose distortions at air-tissue interfaces were observed in the magnetic field's presence. Dose at air-tissue interfaces (chest wall and heart wall) was slightly higher in some patients (3.0% tissue increased by 4.3 Gy on average) compared to nonmagnetic conditions. Average clinical target volume coverage V100 dropped from 99% to 95% compared to reference plans (planEI and planEE). Dose-volume histogram variation between the original plan and reference plans was within 2.3%. Superior-inferior (SI) direction displacement was significantly larger than lateral and anterior-posterior directions (P < .05). Conclusion: Significant SI direction shift in lower esophageal cancerous regions during RT indicates the magnetic field's dosimetric impact, including the electron return effect at tissue-air boundaries. Changes in OAR dose could serve as valuable indicators of organ impairment and target dose alterations, especially for cardiac tissue when using the 1.5 T linac method. Reoptimizing the plan with the magnetic field enhances the feasibility of achieving a clinically acceptable treatment plan for esophageal cancer patients.

A Phase 1b Clinical Trial of Neoadjuvant Radio-immunotherapy for Esophageal Squamous Cell Cancer.

PURPOSE: Neoadjuvant chemoradiotherapy is the recommended treatment for patients with resectable esophageal cancer but is associated with a higher incidence of adverse effects. Given the efficacy of immunotherapy, we propose a chemotherapy-free regimen of neoadjuvant radio-immunotherapy (NRIT) to balance therapeutic efficacy and potential side effects or overtreatment. METHODS AND MATERIALS: In this phase 1b clinical trial, we assessed the safety and efficacy of NRIT in esophageal squamous cell cancer. The enrolled patients received 41.4 Gy of radiation and 4 cycles of 240 mg of toripalimab injection before surgery. The primary endpoint was treatment-related adverse events and the secondary endpoints were pathologic complete response and major pathologic response. Immunohistochemistry and multiplex immunofluorescence staining were used to evaluate the tumor microenvironment before and after neoadjuvant treatment. RESULTS: Of the 22 patients enrolled, 19 underwent R0 surgery. One patient discontinued neoadjuvant immune therapy due to experiencing a grade 3 treatment-related adverse event. Three patients did not undergo surgery due to tumor progression or side effects. Among the patients who underwent surgery, 3 patients experienced serious complications shortly after surgery. Upon pathologic evaluation, the pathologic complete response and major pathologic response rates were 47.4% and 68.4%, respectively. CONCLUSIONS: The NRIT regimen is safe and feasible for patients with esophageal squamous cell cancer.

Surgery versus radiotherapy for limited-stage small cell esophageal carcinoma: a multicenter, retrospective, cohort study in China (ChiSCEC).

BACKGROUND: There is no standard management for small cell esophageal carcinoma (SCEC). The purpose of this multicenter, retrospective study (ChiSCER) was to investigate the treatment, outcomes, and risk factors impacting survival endpoints in patients with limited-stage SCEC (LS-SCEC). MATERIALS AND METHODS: Consecutive patients with LS-SCEC from 14 institutions between 2000 and 2020 in China were enrolled. Survival curves were constructed using the Kaplan-Meier method and compared using a log-rank test. Univariate and multivariate Cox regression models and propensity score matching (PSM) analysis were adopted in the prognostic analysis. Results were reported as hazard ratio (HR), 95% confidence interval (CI), and P value. Statistical significance was set as P value <0.05 in a two-tailed test. RESULTS: Among 458 LS-SCEC patients, the median age was 63 [interquartile range (IQR), 57-68] years, and 318 (69%) were males. Eighty-four (18%), 167 (36%), and 207 (45%) patients received chemotherapy (CT) alone, CT plus definitive radiotherapy (CT+RT), and CT plus radical surgery (CT+S), respectively. With a median follow-up time of 58.7 (95% CI 48.9-68.6) months, the median overall survival (OS) and 3-year OS rate for all patients 24.3 (95% CI 21.6-27) months and 37.3% (95% CI 32.8-42.5%), respectively. Multivariate analysis indicated that treatment modes, Karnofsky performance status (KPS), TNM stage, and CT cycle were independent prognostic factors for OS ( P <0.05). Compared with CT alone, patients treated with CT+RT (HR 0.57, 95% CI 0.41-0.8, P =0.001) or CT+S (HR 0.59, 95% CI 0.42-0.82, P =0.002) had an improved OS, with no significant survival differences between CT+S and CT+RT groups after multivariate and PSM analyses ( P >0.05). Subgroup analysis indicated that compared with CT+RT, patients with tumor location at lower 1/3 (HR 0.59, 95% CI 0.37-0.93, P =0.03) or tumor length >5 cm (HR 0.52, 95% CI 0.3-0.9, P =0.02) could obtain significant OS benefit from CT+S. Patients with tumor location at middle 1/3 (HR 1.55, 95% CI 1.03-2.36, P =0.04) or tumor length ≤5 cm (HR 1.49, 95% CI 1.02-2.17, P =0.04) favored CT+RT. Distant metastasis accounted for 73.7% of all treatment failures after multidisciplinary treatments. CONCLUSION: Surgery and RT were equally effective local therapies for patients with LS-SCEC. The personalized decision of local therapy should be made after comprehensive considerations on tumor location, length, comorbidities, and organ preservation.

Systemic therapy with or without local intervention for oligometastatic oesophageal squamous cell carcinoma (ESO-Shanghai 13): an open-label, randomised, phase 2 trial.

BACKGROUND: The efficacy of local therapy for patients with oligometastatic oesophageal squamous cell carcinoma is unclear. We aimed to assess the efficacy of local plus systemic therapy compared with systemic therapy alone in patients with oligometastatic oesophageal squamous cell carcinoma. METHODS: The ESO-Shanghai 13 trial was a randomised, open-label, multicentre, phase 2 trial. Patients (aged ≥18 years) were recruited from six hospitals in China with histological confirmation of oligometastatic oesophageal squamous cell carcinoma with a controlled primary tumour and one to four metastatic lesions. Eligible patients were randomly assigned via a computer-generated schedule in a 1:1 ratio to receive either systemic therapy alone (ie, systemic therapy only group) or combined systemic and local therapy (ie, systemic and local therapy group). The systemic therapy regimens in both groups were at the discretion of the investigator and included chemotherapy alone, anti-PD-1 antibodies alone, or chemotherapy plus anti-PD-1 antibodies. Local therapy-radiotherapy, surgery, or thermal ablation-was delivered to all metastatic lesions for patients in the systemic and local therapy group. Randomisation was balanced dynamically on three factors: the number of disease sites, the lines of systemic therapy, and the location of the metastases. Patients and investigators were not masked to treatment allocation. The primary endpoint was progression-free survival, defined as the time from randomisation to progression or death from any cause in the intention-to-treat population. The safety population included all patients who had undergone random assignment and at least one of the intended therapies. This trial is registered with ClinicalTrials.gov, NCT03904927. The trial is ongoing but closed to new participants. FINDINGS: 116 patients were screened for enrolment between March 5, 2019, and Sept 16, 2021, and 104 patients who met the eligibility criteria were randomly assigned to the systemic and local therapy group (n=53) or the systemic therapy only group (n=51). 20 (38%) patients in the systemic plus local therapy group and 23 (45%) patients in the systemic therapy only group received anti-PD-1 antibody-based systemic therapy; three patients in the systemic and local therapy group did not receive systemic therapy. At a median follow-up of 30·5 months (IQR 24·7-37·8), median progression-free survival was 15·3 months (95% CI 10·1-20·5) in the systemic and local therapy group versus 6·4 months (5·2-7·6) in the systemic therapy only group (stratified hazard ratio 0·26 [95% CI 0·16-0·42]; stratified log rank p<0·0001). Grade 1-2 acute oesophagitis was more common in the systemic and local therapy group than in the systemic therapy only group (10 [19%] vs one [2%] patients; p=0·036). The number of patients who had grade 3 or worse treatment-related adverse events was similar between groups (25 [47%] vs 21 [41%]; p=0·538), with the most common adverse events being leukocytopenia (17 [32%] vs 18 [35%]) and neutropenia (19 [36%] vs 20 [39%]). Treatment-related deaths occurred in two patients in the systemic and local therapy group and one patient in the systemic therapy only group. INTERPRETATION: The addition of local treatment for metastases could significantly improve progression-free survival among patients with oligometastatic oesophageal squamous cell carcinoma being treated with systemic therapy. Our findings suggest that combining local and systemic therapy could be a treatment option for patients with oligometastatic oesophageal squamous cell carcinoma, but further support from phase 3 trials is required. FUNDING: Science and Technology Commission of Shanghai Municipality, National Nature Science Foundation of China, and Shanghai Municipal Health Commission. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

A glycosylation-related signature predicts survival in pancreatic cancer.

BACKGROUND: Tumor initiation and progression are closely associated with glycosylation. However, glycosylated molecules have not been the subject of extensive studies as prognostic markers for pancreatic cancer. The objectives of this study were to identify glycosylation-related genes in pancreatic cancer and use them to construct reliable prognostic models. MATERIALS AND METHODS: The Cancer Genome Atlas and Gene Expression Omnibus databases were used to assess the differential expression of glycosylation-related genes; four clusters were identified based on consistent clustering analysis. Kaplan-Meier analyses identified three glycosylation-related genes associated with overall survival. LASSO analysis was then performed on The Cancer Genome Atlas and International Cancer Genome Consortium databases to identify glycosylation-related signatures. We identified 12 GRGs differently expressed in pancreatic cancer and selected three genes (SEL1L, TUBA1C, and SDC1) to build a prognostic model. Thereafter, patients were divided into high and low-risk groups. Eventually, we performed Quantitative real-time PCR (qRT-PCR) to validate the signature. RESULTS: Clinical outcomes were significantly poorer in the high-risk group than in the low-risk group. There were also significant correlations between the high-risk group and several risk factors, including no-smoking history, drinking history, radiotherapy history, and lower tumor grade. Furthermore, the high-risk group had a higher proportion of immune cells. Eventually, three glycosylation-related genes were validated in human PC cell lines. CONCLUSION: This study identified the glycosylation-related signature for pancreatic cancer. It is an effective predictor of survival and can guide treatment decisions.

YY1 modulates the radiosensitivity of esophageal squamous cell carcinoma through KIF3B-mediated Hippo signaling pathway.

Radiotherapy is an important strategy in the comprehensive treatment of esophageal squamous cell carcinoma (ESCC). However, effectiveness of radiotherapy is still restricted by radioresistance. Herein, we aimed to understand the mechanisms underlying ESCC radioresistance, for which we looked into the potential role of YY1. YY1 was upregulated in radioresistant tissues and correlated with poor prognosis of patients with ESCC. YY1 depletion enhanced the radiosensitivity of ESCC in vitro and in vivo. Multi-group sequencing showed that downregulation of YY1 inhibited the transcriptional activity of Kinesin Family Member 3B (KIF3B), which further activated the Hippo signaling pathway by interacting with Integrin-beta1 (ITGB1). Once the Hippo pathway was activated, its main effector, Yes-associated protein 1 (YAP1), was phosphorylated in the cytoplasm and its expression reduced in the nucleus, thus enhancing the radiosensitivity by regulating its targeted genes. Our study provides new insights into the mechanisms underlying ESCC radioresistance and highlights the potential role of YY1 as a therapeutic target for ESCC.

[Research progress on the identification of central lung cancer and atelectasis using multimodal imaging].

Central lung cancer is a common disease in clinic which usually occurs above the segmental bronchus. It is commonly accompanied by bronchial stenosis or obstruction, which can easily lead to atelectasis. Accurately distinguishing lung cancer from atelectasis is important for tumor staging, delineating the radiotherapy target area, and evaluating treatment efficacy. This article reviews domestic and foreign literatures on how to define the boundary between central lung cancer and atelectasis based on multimodal images, aiming to summarize the experiences and propose the prospects.

Online prediction for respiratory movement compensation: a patient-specific gating control for MRI-guided radiotherapy.

BACKGROUND: This study aims to validate the effectiveness of linear regression for motion prediction of internal organs or tumors on 2D cine-MR and to present an online gating signal prediction scheme that can improve the accuracy of MR-guided radiotherapy for liver and lung cancer. MATERIALS AND METHODS: We collected 2D cine-MR sequences of 21 liver cancer patients and 10 lung cancer patients to develop a binary gating signal prediction algorithm that forecasts the crossing-time of tumor motion traces relative to the target threshold. Both 0.4 s and 0.6 s prediction windows were tested using three linear predictors and three recurrent neural networks (RNNs), given the system delay of 0.5 s. Furthermore, an adaptive linear regression model was evaluated using only the first 30 s as the burn-in period, during which the model parameters were adapted during the online prediction process. The accuracy of the predicted traces was measured using amplitude metrics (MAE, RMSE, and R2), and in addition, we proposed three temporal metrics, namely crossing error, gating error, and gating accuracy, which are more relevant to the nature of the gating signals. RESULTS: In both 0.6 s and 0.4 s prediction cases, linear regression outperformed other methods, demonstrating significantly smaller amplitude errors compared to the RNNs (P < 0.05). The proposed algorithm with adaptive linear regression had the best performance with an average gating accuracy of 98.3% and 98.0%, a gating error of 44 ms and 45 ms, for liver cancer and lung cancer patients, respectively. CONCLUSION: A functional online gating control scheme was developed with an adaptive linear regression that is both more cost-efficient and accurate than sophisticated RNN based methods in all studied metrics.

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image.

BACKGROUND: The main application of [18F] FDG-PET (18 FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FLART) is desirable but remains challenging. PURPOSE: To develop a deep-learning-based (DL) method to combine 18 FDG-PET and CT images for producing pulmonary perfusion images (PPI). METHODS: Pulmonary technetium-99 m-labeled macroaggregated albumin SPECT (PPISPECT ), 18 FDG-PET, and CT images obtained from 53 patients were enrolled. CT and PPISPECT images were rigidly registered, and registration displacement was subsequently used to align 18 FDG-PET and PPISPECT images. The left/right lung was separated and rigidly registered again to improve the registration accuracy. A DL model based on 3D Unet architecture was constructed to directly combine multi-modality 18 FDG-PET and CT images for producing PPI (PPIDLM ). 3D Unet architecture was used as the basic architecture, and the input was expanded from a single-channel to a dual-channel to combine multi-modality images. For comparative evaluation, 18 FDG-PET images were also used alone to generate PPIDLPET . Sixty-seven samples were randomly selected for training and cross-validation, and 36 were used for testing. The Spearman correlation coefficient (rs ) and multi-scale structural similarity index measure (MS-SSIM) between PPIDLM /PPIDLPET and PPISPECT were computed to assess the statistical and perceptual image similarities. The Dice similarity coefficient (DSC) was calculated to determine the similarity between high-/low- functional lung (HFL/LFL) volumes. RESULTS: The voxel-wise rs and MS-SSIM of PPIDLM /PPIDLPET were 0.78 ± 0.04/0.57 ± 0.03, 0.93 ± 0.01/0.89 ± 0.01 for cross-validation and 0.78 ± 0.11/0.55 ± 0.18, 0.93 ± 0.03/0.90 ± 0.04 for testing. PPIDLM /PPIDLPET achieved averaged DSC values of 0.78 ± 0.03/0.64 ± 0.02 for HFL and 0.83 ± 0.01/0.72 ± 0.03 for LFL in the training dataset and 0.77 ± 0.11/0.64 ± 0.12, 0.82 ± 0.05/0.72 ± 0.06 in the testing dataset. PPIDLM yielded a stronger correlation and higher MS-SSIM with PPISPECT than PPIDLPET (p < 0.001). CONCLUSIONS: The DL-based method integrates lung metabolic and anatomy information for producing PPI and significantly improved the accuracy over methods based on metabolic information alone. The generated PPIDLM can be applied for pulmonary perfusion volume segmentation, which is potentially beneficial for FLART treatment plan optimization.

Sintilimab as maintenance treatment for local/regional recurrent esophageal squamous carcinoma after concurrent chemoradiotherapy: a single-arm Ib/II phase study.

Background: Esophageal cancer (EC) is an aggressive neoplasm of the gastrointestinal tract that is usually treated with a combination of chemotherapy, radiotherapy (RT), and/or surgery, according to disease status. Despite the availability of multimodal therapeutic strategies, local recurrence is frequently observed. However, there is no standard treatment or promising therapeutic approach for local recurrence or metastatic esophageal carcinoma after the RT. This study tended to investigate the efficacy and safety of sintilimab maintenance after concurrent chemoradiotherapy (CCRT) for local/regional recurrent esophageal squamous carcinoma. Methods: This study was a single-arm, phase Ib/II trial conducted in a single site in China. Patients previously radically treated (surgery or CCRT), histologically confirmed, local or regional recurrence esophageal squamous carcinoma, qualified for the study design, were treated with 25-28 times radiotherapy plus raltitrexed once every 3 weeks for up to two cycles. Patients who have not progressed after CCRT received sintilimab as maintenance once every 3 weeks up to 1 year. Primary endpoints were overall survival (OS) and safety. Secondary endpoints were progression-free survival (PFS), objective response rate (ORR), and duration of response (DOR). Results: Between September 2019 and March 2022, in a total of 36 enrolled patients, 34 pts completed CCRT. Three patients excluded due to violation of the exclusion criteria (1 pt) and consent withdrawal (2 pts). Finally, 33 pts were included in the final analysis, in which 3 pts had disease progression, and the remaining 30 entered maintenance therapy with sintilimab. The median follow-up time was 12.3 months. Median OS was 20.6 months (95%CI 10.5-NA) and the 1-year OS rate was 64%. Median PFS was 11.5 months (95%CI 5.29-21.3) and the 1-year PFS rate was 43.6%. The ORR was 63.6% (95%CI 44.6-77.8), including 2 cases of CR and 19 cases of PR. The DCR was 19.9%, the median DOR was 19.5 months, and the median TTR was 2.4 months. The rate of any grade TRAEs was 96.7%; ≥Grade 3 TRAE was 23.4%. The incidence of immune-related AE was 60%, most of which were grade 1-2, and only one case of thyroid-stimulating hormone increased was irAE with grade 3 or above. Conclusion: Sintilimab has shown promising clinical efficacy and a manageable safety profile as maintenance therapy after CCRT for local/regional recurrent esophageal squamous carcinoma. In addition, further confirmation from a large-scale real-world study is still needed.

A nomogram for predicting the rapid progression of diffuse large B-cell lymphoma established by combining baseline PET/CT total metabolic tumor volume, lesion diffusion, and TP53 mutations.

OBJECTIVES: This study aimed to integrate positron emission tomography/computed tomography (PET/CT) metrics and genetic mutations to optimize the risk stratification for diffuse large B-cell lymphoma (DLBCL) patients. METHODS: The data of 94 primary DLBCL patients with baseline PET/CT examination completed in the Shandong Cancer Hospital and Institute (Jinan, China) were analyzed to establish a training cohort. An independent cohort of 45 DLBCL patients with baseline PET/CT examination from other hospitals was established for external validation. The baseline total metabolic tumor volume (TMTV) and the largest distance between two lesions (Dmax) standardized by patient body surface area (SDmax) were calculated. The pretreatment pathological tissues of all patients were sequenced by a lymphopanel including 43 genes. RESULTS: The optimal TMTV cutoff was 285.3 cm3 and the optimal SDmax cutoff was 0.135 m-1 . TP53 status was found as an independent predictive factor significantly affecting complete remission (p = 0.001). TMTV, SDmax, and TP53 status were the main factors of the nomogram and could stratify the patients into four distinct subgroups based on their predicted progression-free survival (PFS). The calibration curve demonstrated satisfactory agreement between the predicted and actual 1-year PFS of the patients. The receiver operating characteristic curves showed this nomogram based on PET/CT metrics and TP53 mutations had a better predictive ability than the clinic risk scores. Similar results were identified upon external validation. CONCLUSIONS: The nomogram based on imaging factors and TP53 mutations could lead to a more accurate selection of DLBCL patients with rapid progression, to increase tailor therapy.

A new risk score model based on lactate dehydrogenase for predicting prognosis in esophageal squamous cell carcinoma treated with chemoradiotherapy.

Background: The prognostic role of lactate dehydrogenase (LDH) has been confirmed in many malignant tumors, but it has not been widely discussed in esophageal squamous cell cancer (ESCC). This study aimed to assess the prognostic value of LDH in patients with ESCC and to generate a risk score model to predict prognosis in patients who were treated with chemoradiotherapy. Methods: A total of 614 patients with ESCC who received chemoradiotherapy from 2012 to 2016 were examined in this single-center retrospective study. The optimal cutoff points for age, cytokeratin 19 fragment antigen 21-1 (Cyfra21-1), carcinoembryonic antigen (CEA), tumor length, total dose, and LDH were calculated by the X-tile software. We analyzed the association between the level of LDH and clinicopathological characteristics, and a 1:3 propensity score matching analysis was used to compensate for differences in baseline characteristics. Kaplan-Meier and Cox regression models were used to determine the prognostic factors for overall survival (OS) and progression-free survival (PFS). Based on the results, we developed a corresponding risk score model and established a nomogram to assess its predictive capacity. Results: The optimal cutoff point of LDH was 134 U/L. Patients in the high-LDH group had significantly shorter PFS and worse OS than did those in the low-LDH group (all P values <0.05). Multivariate survival analysis indicated that pretreatment serum LDH level (P=0.039), Cyfra21-1 level (P=0.003), tumor length (P=0.013), clinical N stage (P=0.047), and clinical M stage (P=0.011) were independent predictors for OS in patients with ESCC who underwent chemoradiotherapy. Furthermore, a risk score model based on these 5 prognostic factors was established to divide patients into 3 prognostic groups to identify those patients with ESCC who were most likely to benefit from chemoradiotherapy (χ2=20.53; P<0.0001). However, the prediction nomogram that integrated the significant independent factors for OS is not performed very well in predicting survival (C-index =0.599). Conclusions: Pretreatment serum LDH level may be a reliable factor in predicting the therapeutic effect of chemoradiotherapy in ESCC. Further validation is needed before this model can be widely used in clinical practice.

Molecular characterization of colorectal mucinous adenocarcinoma and adenocarcinoma, not otherwise specified, identified by multiomic data analysis.

Adenocarcinoma not otherwise specified (AC) and mucinous adenocarcinoma (MC) have different biological behaviors and clinical features. We utilized our previous proteomic data and public transcriptome, single-cell transcriptome, and spatial transcriptome databases to profile the molecular atlas of the tumor microenvironments of MC, AC, and normal colon tissues. By exploring the general and specific molecular features of AC and MC, we found that AC was immune-active but exposed to a hypoxic microenvironment. MC cells could protect against DNA damage, and the microenvironment was unfavorable to leukocyte transendothelial migration. We identified several potential molecular and cellular targets of AC and MC for future research. We also highlighted that the major difference between AC and MC was not the variety of cell types and functions but possibly cell interactions. Stromal and epithelial cell interactions play important roles in both MC and AC, but different regulatory pathways were involved.

FOSL2 promotes intertumoral infiltration of T cells and increases pathological complete response rates in locally advanced rectal cancer patients.

The outcome of neoadjuvant chemoradiotherapy (nCRT) remains highly unpredictable for individuals with locally advanced rectal cancer (LARC). We set out to characterize effective biomarkers that promote a pathological complete response (pCR). We quantified the abundances of 6483 high-confidence proteins in pre-nCRT biopsies of 58 LARC patients from two hospitals with pressure cycling technology (PCT)-assisted pulse data-independent acquisition (PulseDIA) mass spectrometry. Compared with non-pCR patients, pCR patients achieved long-term disease-free survival (DFS) and had higher tumor immune infiltration, especially CD8+ T cell infiltration, before nCRT. FOSL2 was selected as the candidate biomarker for predicting pCR and was found to be significantly upregulated in pCR patients, which was verified in another 54 pre-nCRT biopsies of LARC patients by immunohistochemistry. FOSL2 expression was able to predict pCR by multiple reaction monitoring (MRM) with high efficiency (Area under curve (AUC) = 0.939, specificity = 1.000, sensitivity = 0.850), and high FOSL2 expression was associated with long-term DFS (p = 0.044). When treated with simulated nCRT, FOSL2 sufficiency resulted in more significant inhibition of cell proliferation, and more significant promotion of cell cycle arrest and cell apoptosis. Moreover, CXCL10 secretion with abnormal cytosolic dsDNA accumulation was found in FOSL2-wildtype (FOSL2-WT) tumor cells over nCRT, which might elevate CD8+ T-cell infiltration and CD8+ T-cell-mediated cytotoxicity to promote nCRT-induced antitumor immunity. Our study revealed proteomic profiles in LARC patients before nCRT and highlighted immune activation in the tumors of patients who achieved pCR. We identified FOSL2 as a promising biomarker to predict pCR and promote long-term DFS by contributing to CD8+ T-cell infiltration.