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A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor‐intensive and heavily reliant on the experience of radiation oncologists. In this retrospective study, a novel auto‐segmentation method is proposed to address these problems. To assess the method's applicability across diverse scenarios, we conducted its development and evaluation using a cohort of 148 eligible patients drawn from four multicenter datasets and retrospective data collection including noncontrast CT, multisequence MRI scans, and corresponding medical records. All patients were diagnosed with histologically confirmed high‐grade glioma (HGG). A deep learning‐based method (PKMI‐Net) for automatically segmenting gross tumor volume (GTV) and clinical target volumes (CTV1 and CTV2) of GBMs was proposed by leveraging prior knowledge from multimodal imaging. The proposed PKMI‐Net demonstrated high accuracy in segmenting, respectively, GTV, CTV1, and CTV2 in an 11‐patient test set, achieving Dice similarity coefficients (DSC) of 0.94, 0.95, and 0.92; 95% Hausdorff distances (HD95) of 2.07, 1.18, and 3.95 mm; average surface distances (ASD) of 0.69, 0.39, and 1.17 mm; and relative volume differences (RVD) of 5.50%, 9.68%, and 3.97%. Moreover, the vast majority of GTV, CTV1, and CTV2 produced by PKMI‐Net are clinically acceptable and require no revision for clinical practice. In our multicenter evaluation, the PKMI‐Net exhibited consistent and robust generalizability across the various datasets, demonstrating its effectiveness in automatically segmenting GBMs. The proposed method using prior knowledge in multimodal imaging can improve the contouring accuracy of GBMs, which holds the potential to improve the quality and efficiency of GBMs' radiotherapy. [ABSTRACT FROM AUTHOR]

Objective To compare curative effect between lenvatinib combined with locoregional therapy and locoregional therapy on PD-L1-positive hepatocellular carcinoma patients with type Ⅰ-Ⅲ portal vein tumor thrombus according to Cheng's classification. Methods The patients in lenvatinib combined with locoregional therapy group received orally-administered lenvatinib at a dose of 12 mg qd for patients≥60 kg or 8 mg qd for patients < 60 kg. The locoregional therapy group only received locoregional therapy. We retrospectively analyzed the clinical data and prognosis of two groups. Results The CR+PR were 78.1% and 53.6% in the combination group and locoregional therapy group, respectively (P < 0.05). The response rate, disease control rate and overall survival of the combination group were higher than those in the locoregional therapy group (P < 0.05). Conclusion The curative effect and overall survival of lenvatinib combined with locoregional therapy is better than locoregional therapy on PD-L1-positive hepatocellular carcinoma patients with type Ⅰ-Ⅲ portal vein tumor thrombus according to Cheng's classification.

Objective To evaluate the prognosis-related factors of colorectal cancer patients with positive PD-L1 expression in liver metastases after hepatectomy. Methods We reviewed retrospectively the clinical data of 68 colorectal cancer patients with positive PD-L1 expression in liver metastases receiving personalized comprehensive treatment which was mainly consisted of surgical resection. We observed the results and prognosis after surgical resection and analyzed related factors. Results Univariate analysis showed that no radiotherapy, N stage, RAS mutation status, T stage, dMMR, Duck stage, disease free interval from primary to metastases≤12 months and largest hepatic tumor diameter > 5 cm had obvious significance (all P < 0.05). Multivariate logistic analysis regression revealed that without dMMR (P=0.012), Duck A stage (P=0.000), disease free interval from primary to metastases > 12 months (P=0.020) and largest hepatic tumor diameter < 5 cm (P=0.006) were independent protective factors for the prognosis of colorectal cancer patients with positive PD-L1 expression in liver metastases after surgical resection. Conclusion Personalized comprehensive treatment which is mainly consisted of surgical resection still has a good effect on colorectal cancer patients with positive PD-L1 expression in liver metastases after hepatectomy. Without dMMR, Duck A stage, disease free interval from primary to metastases > 12 months and largest hepatic tumor diameter ≤5cm are independent protective factors for patients' prognosis.

Purpose: To investigate the safety and efficacy of CT-guided I125 radioactive seed implantation (RSI) as a salvage therapy for recurrent head and neck squamous carcinoma (rHNSC) after external beam radiotherapy (EBRT) or surgery. Materials and Methods: This is a multicenter retrospective study of 113 patients (83 males; median age 57 years) with rHNSC who underwent CT-guided I125 RSI between February 2003 and December 2017. Of the included patients, 107 patients previously received EBRT and 65 patients received surgery and all were ineligible or rejected for salvage surgery and/or repeat EBRT. Results: During a median follow-up duration of 20 months (range, 3-152 months), 87 patients died. The 1-, 2-, 3-, and 5-year local control rate were 57.4%, 41.8%, 29.3%, and 15.2%, respectively. The median time to progression was 15 months [95% confidence interval (CI), 6.1-23.9 months]. The median overall survival (OS) was 20 months (95% CI, 12.4-27.6 months). The 1-, 2-, 3-, and 5-year OS rate were 63.6%, 44.6%, 29.9%, and 21.7%, respectively. Univariate and multivariate analyses revealed that KPS score and postoperative D90 were significantly associated with patients' OS. The complications were mainly grade I/II skin and mucosal reactions: 18 cases (15.9%) of grade I/II and eight cases (7.0%) of grade III radiation dermatitis, and 14 cases (12.4%) of grade I/II and three cases (2.7%) grade III mucosal reactions. No grade IV or severer complications were found. Conclusion: CT-guided I125 RSI may be safe as a salvage therapy for rHNSC after EBRT/surgery, yielding promising efficacy compared with historical data. KPS score and postoperative D90 may be significantly associated with OS. [ABSTRACT FROM AUTHOR]

Abstract Background To assess the feasibility and efficacy of using 125I seed implantation under intraoperative ultrasound guidance for unresectable pancreatic carcinoma. Methods Fourteen patients with pancreatic carcinoma that underwent laparotomy and considered unresectable were included in this study. Nine patients were pathologically diagnosed with Stage II disease, five patients with Stage III disease. Fourteen patients were treated with 125I seed implantation guided by intraoperative ultrasound and received D90 of 125I seeds ranging from 60 to 140 Gy with a median of 120 Gy. Five patients received an additional 35–50 Gy from external beam radiotherapy after seed implantation and six patients received 2–6 cycles of chemotherapy. Results 87.5% (7/8) of patients received partial to complete pain relief. The response rate of tumor was 78.6%, One-, two-and three-year survival rates were 33.9% and 16.9%, 7.8%, with local control of disease achieved in 78.6% (11/14), and the median survival was 10 months (95% CI: 7.7–12.3). Conclusion There were no deaths related to 125I seed implant. In this preliminary investigation, 125I seed implant provided excellent palliation of pain relief, local control and prolong the survival of patients with stage II and III disease to some extent.

The aim of this study was to investigate the safety and accuracy of computed tomography (CT)-guided 125I seed implantation assisted by a three-dimensional printing non-coplanar template (3D-PNCT) for treating pelvic locally recurrent rectal cancer (LRRC) patients. A total of 13 patients with 18 masses received 125I seed implantation. The dosimetric parameters of pre-implantation and post-implantation were calculated to evaluate the quality of 125I seed implantation. Doses delivered to the organs at risk (OAR) were also calculated. Differences between pre-implantation and post-implantation were compared by the paired t -test. The mean number of 125I seeds pre-implantation and post-implantation was 67.1 and 68.8, respectively. The mean values of D90 (dose that was delivered to 90% of the target volume), D100 (dose that was delivered to 100% of the target volume), V100 (the target volume receiving 100% of the prescription dose) and V150 (the target volume receiving 150% of the prescription dose) pre-implantation and post-implantation were 136.6 and 135.2 Gy, 63.5 and 71.0 Gy, 90.3% and 90.3% and 62.1% and 62.2%, respectively. Dosimetric outcomes were evaluated quantitatively using the dose volume indices, i.e. coverage index (CI), external volume index (EI) and relative dose homogeneity index (HI). The mean values of those indices pre-implantation and post-implantation were 0.62 and 0.61, 0.31 and 0.33, and 0.31 and 0.31, respectively. The mean doses delivered to OAR pre-implantation and post-implantation for the bladder (D2cc) and bowel (D2cc) were 33.4 and 34.4 Gy, and 58.6 and 61.8 Gy, respectively. The parameters mentioned above fitted well, and no significant difference was found among them. It is concluded that CT-guided 125I seed implantation assisted by 3D-PNCT could be a safe and accurate salvage modality for treating LRRC patients; the ideal pre-prescription dose could be achieved. Also, addition of 3D-PNCT could minimize radiation damage to the surrounding normal tissues. [ABSTRACT FROM AUTHOR]

For deep learning networks used to segment organs at risk (OARs) in head and neck (H&N) cancers, the class-imbalance problem between small volume OARs and whole computed tomography (CT) images results in delineation with serious false-positives on irrelevant slices and unnecessary time-consuming calculations. To alleviate this problem, a slice classification model-facilitated 3D encoder–decoder network was developed and validated. In the developed two-step segmentation model, a slice classification model was firstly utilized to classify CT slices into six categories in the craniocaudal direction. Then the target categories for different OARs were pushed to the different 3D encoder–decoder segmentation networks, respectively. All the patients were divided into training (n  = 120), validation (n  = 30) and testing (n  = 20) datasets. The average accuracy of the slice classification model was 95.99%. The Dice similarity coefficient and 95% Hausdorff distance, respectively, for each OAR were as follows: right eye (0.88 ± 0.03 and 1.57 ± 0.92 mm), left eye (0.89 ± 0.03 and 1.35 ± 0.43 mm), right optic nerve (0.72 ± 0.09 and 1.79 ± 1.01 mm), left optic nerve (0.73 ± 0.09 and 1.60 ± 0.71 mm), brainstem (0.87 ± 0.04 and 2.28 ± 0.99 mm), right temporal lobe (0.81 ± 0.12 and 3.28 ± 2.27 mm), left temporal lobe (0.82 ± 0.09 and 3.73 ± 2.08 mm), right temporomandibular joint (0.70 ± 0.13 and 1.79 ± 0.79 mm), left temporomandibular joint (0.70 ± 0.16 and 1.98 ± 1.48 mm), mandible (0.89 ± 0.02 and 1.66 ± 0.51 mm), right parotid (0.77 ± 0.07 and 7.30 ± 4.19 mm) and left parotid (0.71 ± 0.12 and 8.41 ± 4.84 mm). The total segmentation time was 40.13 s. The 3D encoder–decoder network facilitated by the slice classification model demonstrated superior performance in accuracy and efficiency in segmenting OARs in H&N CT images. This may significantly reduce the workload for radiation oncologists. [ABSTRACT FROM AUTHOR]