Objective This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). Methods We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. Results For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). Conclusion Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.

Objective To investigate the effect of various frequencies of bolus use on the superficial dose of volumetric modulated arc therapy after modified radical mastectomy for breast cancer. Methods Based on the computed tomography images of a female anthropomorphic breast phantom, a 0.5 cm silicone-based 3D-printed bolus was created. Nine points evenly distributed on the breast skin were selected for assessing the skin dose, and a volume of subcutaneous lymphatic drainage of the breast (noted as ROI2-3) was delineated for assessing the chest wall dose. The treatment plans with and without bolus (plan_wb and plan_nb) were separately designed using the prescription of 50 Gy in 25 fractions following the standard dose constraints of the adjacent organ at risk. To characterize the accuracy of treatment planning system (TPS) dose calculations, the doses of the nine points were measured five times by thermoluminescence dosimeters (TLDs) and then were compared with the TPS calculated dose. Results Compared with Plan_nb (144.46 ± 10.32 cGy), the breast skin dose for plan_wb (208.75 ± 4.55 cGy) was significantly increased (t = −18.56, P < 0.001). The deviation of skin dose was smaller for Plan_wb, and the uniformity was significantly improved. The calculated value of TPS was in good agreement with the measured value of TLD, and the maximum deviation was within 5%. Skin and ROI2-3 doses were significantly increased with increasing frequencies of bolus applications. The mean dose of the breast skin and ROI2-3 for 15 and 23 times bolus applications were 45.33 Gy, 50.88 Gy and 50.36 Gy, 52.39 Gy, respectively. Conclusion 3D printing bolus can improve the radiation dose and the accuracy of the planned dose. Setting Plan_wb to 15 times for T1-3N+ breast cancer patients and 23 times for T4N+ breast cancer patients can meet the clinical need. Quantitative analysis of the bolus application frequency for different tumor stages can provide a reference for clinical practice.

Abstract Purpose The study aimed to predict acute radiation esophagitis (ARE) with grade ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics. Methods 161 patients with stage IIIA−IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose−volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts (N = 107) and testing cohorts (N = 54). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training–testing splits. Their performances were assessed using the area under the ROC curve (AUC) and accuracy. Results Among all patients, 51 developed ARE grade ≥ 2, with an incidence of 31.7%. Next, 8990 radiomics and 213 dosiomics features were extracted, and 3, 6, 12, and 13 features remained after feature selection in the CF, DF, RF and DF models, respectively. The RF and HF models achieved similar classification performance, with the training and testing AUCs of 0.796 ± 0.023 (95% confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 (95% CI [0.73, 0.76]) and 0.801 ± 0.022 (95% CI [0.79, 0.81]) (p = 0.74), respectively. The model performances using CF and DF features were poorer, with training and testing AUCs of 0.573 ± 0.026 (95% CI [0.56, 0.58])/ 0.509 ± 0.072 (95% CI [0.48, 0.53]) and 0.679 ± 0.027 (95% CI [0.67, 0.69])/0.604 ± 0.041 (95% CI [0.53, 0.63]) compared with the above two models (p < 0.001), respectively. Conclusions In LALC patients treated with CRT IMRT, the ARE grade ≥ 2 can be predicted using the pretreatment radiotherapy image features. To predict ARE, the multi-omics features had similar predictability with radiomics features; however, the dosiomics features and clinical factors had a limited classification performance.

Purpose: This study investigates the impact of lung function on radiation pneumonitis prediction using a dual-omics analysis method.Methods: We retrospectively collected data of 126 stage III lung cancer patients treated with chemo-radiotherapy using intensity-modulated radiotherapy, including pre-treatment planning CT images, radiotherapy dose distribution, and contours of organs and structures. Lung perfusion functional images were generated using a previously developed deep learning method. The whole lung (WL) volume was divided into function-wise lung (FWL) regions based on the lung perfusion functional images. A total of 5,474 radiomics features and 213 dose features (including dosiomics features and dose-volume histogram factors) were extracted from the FWL and WL regions, respectively. The radiomics features (R), dose features (D), and combined dual-omics features (RD) were used for the analysis in each lung region of WL and FWL, labeled as WL-R, WL-D, WL-RD, FWL-R, FWL-D, and FWL-RD. The feature selection was carried out using ANOVA, followed by a statistical F-test and Pearson correlation test. Thirty times train-test splits were used to evaluate the predictability of each group. The overall average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and f1-score were calculated to assess the performance of each group.Results: The FWL-RD achieved a significantly higher average AUC than the WL-RD group in the training (FWL-RD: 0.927 ± 0.031, WL-RD: 0.849 ± 0.064) and testing cohorts (FWL-RD: 0.885 ± 0.028, WL-RD: 0.762 ± 0.053, p < 0.001). When using radiomics features only, the FWL-R group yielded a better classification result than the model trained with WL-R features in the training (FWL-R: 0.919 ± 0.036, WL-R: 0.820 ± 0.052) and testing cohorts (FWL-R: 0.862 ± 0.028, WL-R: 0.750 ± 0.057, p < 0.001). The FWL-D group obtained an average AUC of 0.782 ± 0.032, obtaining a better classification performance than the WL-D feature-based model of 0.740 ± 0.028 in the training cohort, while no significant difference was observed in the testing cohort (FWL-D: 0.725 ± 0.064, WL-D: 0.710 ± 0.068, p = 0.54).Conclusion: The dual-omics features from different lung functional regions can improve the prediction of radiation pneumonitis for lung cancer patients under IMRT treatment. This function-wise dual-omics analysis method holds great promise to improve the prediction of radiation pneumonitis for lung cancer patients.

Purpose: To evaluate the effectiveness of features obtained from our proposed incremental-dose-interval-based lung subregion segmentation (IDLSS) for predicting grade ≥ 2 acute radiation pneumonitis (ARP) in lung cancer patients upon intensity-modulated radiotherapy (IMRT). (1) Materials and Methods: A total of 126 non-small-cell lung cancer patients treated with IMRT were retrospectively analyzed. Five lung subregions (SRs) were generated by the intersection of the whole lung (WL) and five sub-regions receiving incremental dose intervals. A total of 4610 radiomics features (RF) from pre-treatment planning computed tomographic (CT) and 213 dosiomics features (DF) were extracted. Six feature groups, including WL-RF, WL-DF, SR-RF, SR-DF, and the combined feature sets of WL-RDF and SR-RDF, were generated. Features were selected by using a variance threshold, followed by a Student t-test. Pearson’s correlation test was applied to remove redundant features. Subsequently, Ridge regression was adopted to develop six models for ARP using the six feature groups. Thirty iterations of resampling were implemented to assess overall model performance by using the area under the Receiver-Operating-Characteristic curve (AUC), accuracy, precision, recall, and F1-score. (2) Results: The SR-RDF model achieved the best classification performance and provided significantly better predictability than the WL-RDF model in training cohort (Average AUC: 0.98 ± 0.01 vs. 0.90 ± 0.02, p < 0.001) and testing cohort (Average AUC: 0.88 ± 0.05 vs. 0.80 ± 0.04, p < 0.001). Similarly, predictability of the SR-DF model was significantly stronger than that of the WL-DF model in training cohort (Average AUC: 0.88 ± 0.03 vs. 0.70 ± 0.030, p < 0.001) and in testing cohort (Average AUC: 0.74 ± 0.08 vs. 0.65 ± 0.06, p < 0.001). By contrast, the SR-RF model significantly outperformed the WL-RF model only in the training set (Average AUC: 0.93 ± 0.02 vs. 0.85 ± 0.03, p < 0.001), but not in the testing set (Average AUC: 0.79 ± 0.05 vs. 0.77 ± 0.07, p = 0.13). (3) Conclusions: Our results demonstrated that the IDLSS method improved model performance for classifying ARP with grade ≥ 2 when using dosiomics or combined radiomics-dosiomics features.

Radiomics and dosiomics as two kinds of imaging features are widely used for machine learning-based prognosis prediction in adaptive radiotherapy. Feature selection and modeling are two main components in the radiotherapy prognosis prediction pipeline. So far, few studies have considered both the stability and discrimination ability of the features at the stage of feature selection. Also, in the modeling phase, to fuse radiomics and dosiomics features, most works have only directly concatenated radiomics or dosiomics features as inputs into a model, which may omit the complementary information across different omics. Additionally, overfitting is a common issue when the training data is not enough or contains noises. To solve these problems, in this study, we have developed a novel machine learning-based pipeline and applied it to predict radiation pneumonitis for stage III non-small cell lung cancer patients under medical Internet of Things. The contributions contain the following: in the feature selection phase, a decision criterion which considers both the feature stability and feature discrimination is developed to determine appropriate feature selection methods; in the modeling phase, we have developed a non-sparse multi-kernel learning method with manifold regularization for multi-omics fusion, which can fully explore patterns from both radiomics and dosiomics features and reduce overfitting coincidently. Experimental results show that the decision criterion works effectively for feature selection method selection. Compared to direct feature concatenation, the proposed multi-kernel fusion strategy performs better. Moreover, manifold regularization can alleviate the overfitting problem.

Purpose: This study aimed to evaluate the utility of a new plan feature (planomics feature) for predicting the results of patient-specific quality assurance using the head and neck (H&N) volumetric modulated arc therapy (VMAT) plan.Methods: One hundred and thirty-one H&N VMAT plans in our institution from 2019 to 2021 were retrospectively collected. Dosimetric verification for all plans was carried out using the portal dosimetry system integrated into the Eclipse treatment planning system based on the electronic portal imaging devices. Gamma passing rates (GPR) were analyzed using three gamma indices of 3%/3 mm, 3%/2 mm, and 2%/2 mm with a 10% dose threshold. Forty-eight conventional features affecting the dose delivery accuracy were used in the study, and 2,476 planomics features were extracted based on the radiotherapy plan file. Three prediction and classification models using conventional features (CF), planomics features (PF), and hybrid features (HF) combining two sets of features were constructed by the gradient boosting regressor (GBR) and Ridge classifier for each GPR of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. The absolute prediction error (APE) and the area under the curve (AUC) were adopted for assessing the performance of prediction and classification models.Results: In the GPR prediction, the average APE of the models using CF, PF, and HF was 1.3 ± 1.2%/3.6 ± 3.0%, 1.7 ± 1.5%/3.8 ± 3.5%, and 1.1 ± 1.0%/4.1 ± 3.1% for 2%/2 mm; 0.7 ± 0.6%/2.0 ± 2.0%, 1.0±1.1%/2.2 ± 1.8%, and 0.6 ± 0.6%/2.2 ± 1.9% for 3%/2 mm; and 0.4 ± 0.3%/1.2 ± 1.2%, 0.4±0.5%/1.3 ± 1.0%, and 0.3±0.3%/1.2 ± 1.1% for 3%/3 mm, respectively. In the regression prediction, three models give a similar modeling performance for predicting the GPR. The classification results were 0.67 ± 0.03/0.66 ± 0.07, 0.77 ± 0.03/0.73 ± 0.06, and 0.78 ± 0.02/0.75 ± 0.04 for 3%/3 mm, respectively. For 3%/2 mm, the AUCs of the training and testing cohorts were 0.64 ± 0.03/0.62 ± 0.07, 0.70 ± 0.03/0.67 ± 0.06, and 0.75 ± 0.03/0.71 ± 0.07, respectively, and for 2%/2 mm, the average AUCs of the training and testing cohorts were 0.72 ± 0.03/0.72 ± 0.06, 0.78 ± 0.04/0.73 ± 0.07, and 0.81 ± 0.03/0.75 ± 0.06, respectively. In the classification, the PF model has a better classification performance than the CF model. Moreover, the HF model provides the best result among the three classifications models.Conclusions: The planomics features can be used for predicting and classifying the GPR results and for improving the model performance after combining the conventional features for the GPR classification.

Background. We investigated the efficacy of TILs and anti-PD1 combination therapy in patients with metastatic cervical cancer with low MSI expression and PDL1-negative. Methods. A total of 80 patients were put on TILs and anti-PD1 combination therapy, and the progression-free survival time (PFS) and overall survival time (OS) were assessed by Kaplan–Meier analysis. Univariate and multivariate analyses were performed to identify factors that could predict the prognosis of metastatic cervical cancer in the previously described patients. Results. The objective response rate was 25%, whereas the mPFS and mOS were 6.1 and 11.3 months, respectively. The therapeutic efficacy was influenced by the characteristics of TILs, infection with HPV, and development of fever just after the therapy. Conclusion. Overall, our results show that the combination therapy of TILs and anti-PD1 significantly improves the prognosis of metastatic cervical cancer.

Background: Esophageal cancer (EC) is one of the most common malignant tumors in the world. Due to difficulties with performing the operation, most patients choose to have palliative treatment instead. Radiotherapy is one of the main palliative treatments of EC. However, the clinical efficacy of radiotherapy is not satisfactory α-Solanine is a bioactive component of steroidal glycoalkaloids which has been demonstrated to exhibit anti-metastasis activity in different cancers. In the present study, we determined the effect of α-solanine on the radiosensitivity of EC cells and priliminarily explored the underlying molecular mechanisms. Methods: Cell Counting Kit-8 (CCK-8) assay was conducted to found the cytotoxic effect of α-solanine on EC cells. CCK-8 assay and colony-forming survival assays were performed to explore the effect of α-solanine on cell viability and proliferation of EC cells after irradiation. Immunofluorescence and comet assays were used to detect the effect of α-solanine on DNA repair capacity of EC cells after irradiation. The flow cytometry (FCM) and Hoechst/PI staining were conductd to study the effect of α-solanine on apoptosis of EC cells after irradiation. Results: The cytotoxic effect of α-solanine to EC cells was dose-dependent. The results of CCK-8, colony-forming survival assay, immunofluorescence, comet assay, FCM and Hoechst/PI staining showed that α-solanine could enhance the radiosensitivity of EC cells. α-Solanine could downregulate Survivin expression level by upregulating miR-138 expression in EC cells. Upregulation of miR-138 and knock down Survivin both enhanced the radiosensitivity of EC cells. Moreover, Survivin could restore the effect of α-solanine and miR-138 on radiosensitivity of EC cells. Conclusions: α-solanine could enhance the radiosensitivity of esophageal cancer cells by inducing microRNA-138 expression, and probably be an effective radiosensitizer in treating EC.

Activating enhancer-binding protein (AP)-4 is a member of the basic helix-loop-helix transcription factors, and is involved in tumor biology. However, the role of AP-4 in human lung cancer remains to be fully elucidated. In the present study, the expression of AP-4 in human lung cancer tissues and cells was investigated by reverse transcription-quantitative polymerase chain reaction, and it was observed that the level of AP-4 was increased in tumor tissues and cells compared with their normal counterparts. AP-4 expression was knocked down by transfection with a specific small interfering RNA (siRNA) in lung cancer cells, and this indicated that siRNA-mediated silencing of AP-4 inhibited cell proliferation, arrested the cell cycle at the G0/G1 phase and induced apoptosis by modulating the expression of p21 and cyclin D1. The results of the present study suggest that AP-4 may be an oncoprotein that has a significant role in lung cancer, and that siRNA-mediated silencing of AP-4 may have therapeutic potential as a strategy for the treatment of lung cancer.

Human DNA polymerase β (polβ) is a small monomeric protein that is essential for short-patch base excision repair. It plays an important role in regulating the sensitivity of tumor cells to chemotherapy. We have previously identified a G to C point mutation at nucleotide 648 (G648C) of polβ in esophageal cancer (EC). In this study, we evaluated the mutation of polβ in a larger cohort of EC patients by RT-PCR and sequencing analysis. The function of the mutation was evaluated by MTT, in vivo tumor growth, and flow cytometry assays. The G648C mutation occurred in 15 (3.45 %) of 435 EC patients. In addition, patients with this mutation had significantly longer survival time than those without, following postoperative chemotherapy. Cell lines with G648C mutation in polβ gene were more sensitive to treatment with 5-fluorouracil and cisplatin than those with wild-type polβ. These results suggest that polβ gene with G648C mutation in surgically resected esophagus may be clinically useful for predicting responsiveness to chemotherapy in patients with EC. The polβ gene alteration may serve as a prognostic biomarker for EC.

Deregulation of microRNAs (miRNAs) is implicated in tumor progression. We attempt to identify the association between miR-138 and Sentrin/SUMO-specific protease 1 (SENP1) as a radiosensitization-related gene and characterize the biological function by which SENP1 was regulated by miR-138 to influence radiosensitization in lung cancer cells. In this study, we showed that miRNA-138 is reduced in both lung cancer clinical specimens and cell lines and is effective to inhibit SENP1 expression. Moreover, high levels of miR-138 are associated with lower levels of lung cancer cell proliferation and colony formation. Then, we investigated the underlying mechanisms responsible for the increase in the radiosensitivity of lung cancer cells when SENP1 is inhibited by miR-138. We further show that the increased radiosensitivity may be the result of an increased γ-H2AX expression, an increased rate of apoptosis, and changes in the cell cycle. In conclusion, our data demonstrate that the miR-138/SENP1 cascade is relative to radiosensitization in lung cancer cells and is a potential radiotherapy target.

Purpose. To determine interfractional changes of lung tumor centroid position and tumor regression during stereotactic body radiation therapy (SBRT). Methods and Materials. 34 patients were treated by SBRT in 4-5 fractions to a median dose of 50 Gy. The CT scans acquired for verification were registered with simulation CT scans. The gross target volume (GTV) was contoured on all verification CT scans and compared to the initial GTV in treatment plan system. Results. The mean (±standard deviation, SD) three-dimension vector shift was 5.2±3.1 mm. The mean (±SD) interfractional variations of tumor centroid position were -0.7±4.5 mm in anterior-posterior (AP) direction, 0.2±3.1 mm in superior-inferior (SI) direction, and 0.4±2.4 mm in right-left (RL) direction. Large interfractional variations (≥5 mm) were observed in 5 fractions (3.3%) in RL direction, 16 fractions (10.5%) in SI direction, and 36 fractions (23.5%) in AP direction. Tumor volume did not decrease significantly during lung SBRT. Conclusions. Small but insignificant tumor volume regression was observed during lung SBRT. While the mean interfractional variations of tumor centroid position were minimal in three directions, variations more than 5 mm account for approximately a third of all, indicating additional margin for PTV, especially in AP direction.

Background Flavonoids are structurally heterogeneous, polyphenolic compounds present in high concentrations in fruits, vegetables, and other plant-derived foods. Currently, there is growing interest in the therapeutic applications of bioflavonoids for the treatment and prevention of diseases in humans. Myricetin is a naturally occurring flavonoid that is commonly found in tea, berries, fruits, vegetables, and medicinal herbs. Previous studies have shown that myricetin has antioxidant, anti-inflammatory and potent anticancer effects. It was interesting to investigate whether myricetin has the cooperative inhibitory effect combined with 5-fluorouracil on esophageal cancer cells. Methods EC9706 cells were treated with 5-fluorouracil combination with or without myricetin. Colony formation assays, CCK-8 assay and flow cytometry were used to evaluate the chemosensitization activity of myricetin combine with 5-fluorouracil on the cell growth and viability, cell proliferation and apoptosis in vitro. Western blot was engaged to detect changes of Survivin, Cyclin D, Bcl-2, Caspase-3 and P53 protein expression level, which were associated with cells proliferation and apoptosis. Nude mouse tumor xenograft model was built to assessed chemosensitization effect of myricetin combine with 5-fluorouracil in vivo. Results Compared with the 5-fluorouracil group without myricetin treatment, the groups treated with 5-fluorouracil combine with myricetin showed significantly suppressed cell survival fraction and proliferation, increased the cell apoptosis. Decreased Survivin, Cyclin D, Bcl-2, and increased Caspase-3, P53 expression level were aslo confirmed by western blot in 5-fluorouracil combine with myricetin groups in vitro. And in vivo assay, growth speed of tumor xenografts was significantly decreased in the mice treated with 5-fluorouracil + myricetin combiantion group. Conclusions The study demonstrated both in vitro and in vivo evidence that combination of myricetin with 5-fluorouracil chemotherapy can enhance tumor chemosensitivity of esophageal cancer EC9706 cells, and myricetin could be a potential chemosensitizer for esophageal cancer therapy.

MicroRNAs have emerged as important gene regulators and are recognized as important molecules in carcinogenesis. However, the effects of microRNA-1303 (miR-1303) on gastric cancer (GC) cells and the upstream regulation of GC-associated claudin-18 gene (CLDN18) remain unclear. miR-1303 may be involved in the tumorigenesis of GC by targeting CLDN18.