Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning.

PURPOSE: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool. METHODS: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan models developed in-house. The autoplans were, then, applied to the original, physician-drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA). Using a "two one-sided tests" (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria. RESULTS: For HN plans, 91.8% and 91.7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively. CONCLUSIONS: This study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances.

Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.

PURPOSE: Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world. METHODS: The RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale. RESULTS: For cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%). CONCLUSION: The RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics.

Automatic end-to-end VMAT treatment planning for rectal cancers.

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. METHODS: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. RESULTS: In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. CONCLUSIONS: This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.

The modified cross-suture technique for loop stoma reversal.

TECHNIQUE: (1) A four-pointed star-shaped incision was made to separate the skin around the stoma intestine. (2) The stoma intestine was resected, and side-to-side or end-to-side anastomosis was performed to restore the continuity of the intestine. (3) The peritoneum and rectus sheath should be closed using continuous full-thickness sutures. (4) The subcutaneous fat layer and dermis layer should be sutured using purse-string sutures. Two holes should be made in the center of the sutured area. (5) The cross should be sutured intermittently on all four sides using 1-2 stitches. (6) A rubber strip should be placed in the center of the small hole. RESULTS: The presence of a small hole in the center of the incision and the use of a rubber strip for drainage facilitate early fluid drainage. The design of a cross-stitched skin incision helps reduce local tension. CONCLUSION: The modified cross-suture technique may reduce postoperative incision infections and associated pain, which is a suitable incision treatment method for loop stoma reversal.

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources.

Access to radiotherapy worldwide is limited. The Radiation Planning Assistant (RPA) is a fully automated, web-based tool that is being developed to offer fully automated radiotherapy treatment planning tools to clinics with limited resources. The goal is to help clinical teams scale their efforts, thus reaching more patients with cancer. The user connects to the RPA via a webpage, completes a Service Request (prescription and information about the radiotherapy targets), and uploads the patient's CT image set. The RPA offers two approaches to automated planning. In one-step planning, the system uses the Service Request and CT scan to automatically generate the necessary contours and treatment plan. In two-step planning, the user reviews and edits the automatically generated contours before the RPA continues to generate a volume-modulated arc therapy plan. The final plan is downloaded from the RPA website and imported into the user's local treatment planning system, where the dose is recalculated for the locally commissioned linac; if necessary, the plan is edited prior to approval for clinical use.

Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS: Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS: Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION: DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.

Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant.

PURPOSE: Automation, including the use of artificial intelligence, has been identified as a possible opportunity to help reduce the gap in access and quality for radiotherapy and other aspects of cancer care. The Radiation Planning Assistant (RPA) project was conceived in 2015 (and funded in 2016) to use automated contouring and treatment planning algorithms to support the efforts of oncologists in low- and middle-income countries, allowing them to scale their efforts and treat more patients safely and efficiently (to increase access). DESIGN: In this review, we discuss the development of the RPA, with a particular focus on clinical acceptability and safety/risk across jurisdictions as these are important indicators for the successful future deployment of the RPA to increase radiotherapy availability and ameliorate global disparities in access to radiation oncology. RESULTS: RPA tools will be offered through a webpage, where users can upload computed tomography data sets and download automatically generated contours and treatment plans. All interfaces have been designed to maximize ease of use and minimize risk. The current version of the RPA includes automated contouring and planning for head and neck cancer, cervical cancer, breast cancer, and metastases to the brain. CONCLUSION: The RPA has been designed to bring high-quality treatment planning to more patients across the world, and it may encourage greater investment in treatment devices and other aspects of cancer treatment.

Photosynthetic response mechanism to polybrominated diphenyl ether exposure in Chlorella pyrenoidosa.

Polybrominated diphenyl ether (PBDE) contamination is common in aquatic environments and can severely damage aquatic organisms. However, there is a lack of information on the response and self-adaptation mechanisms of these organisms. Chlorella pyrenoidosa was treated with 2,2',4,4'-tetrabromodiphenyl ether (BDE47), causing significant growth inhibition, pigment reduction, oxidative stress, and chloroplast atrophy. Photosynthetic damage contributed to inhibition, as indicated by Fv/Fm, Chl a fluorescence induction, photosynthetic oxygen evolution activity, and photosystem subunit stoichiometry. Here, Chl a fluorescence induction and quinone electron acceptor (QA-) reoxidation kinetics showed that the PSII donor and acceptor sides were insensitive to BDE47. Quantitative analyses of D1 and PsaD proteins illustrated that PSII and PSI complexes were the main primary targets of photosynthesis inhibition by BDE47. Significant modulation of PSII complex might have been caused by the potential binding of BDE47 on D1 protein, and molecular docking was performed to investigate this. Increased activation of antioxidant defense systems and photosystem repair as a function of exposure time indicated a positive resistance to BDE47. After a 5-day exposure, 23 % of BDE47 was metabolized. Our findings suggest that C. pyrenoidosa has potential as a bioremediator for wastewater-borne PBDEs and can improve our understanding of ecological risks to microalgae.

Commissioning and dosimetric validation of a novel compensator-based Co-60 IMRT system for evaluating suitability to automated treatment planning.

PURPOSE: A novel compensator-based system has been proposed which delivers intensity-modulated radiation therapy (IMRT) with cobalt-60 beams. This could improve access to advanced radiotherapy in low- and middle-income countries. For this system to be clinically viable and to be adapted into the Radiation Planning Assistant (RPA), being developed to offer automated planning services in low- and middle-income countries, it is necessary to commission and validate it in a commercial treatment planning system (TPS). METHODS: The novel treatment device considered here employs a cobalt-60 source and nine compensators. Each compensator is produced by 3-D printing a thin plastic mold which is then filled on-demand within the machine with reusable 2-mm-diameter spherical tungsten balls. This system was commissioned in the Eclipse TPS and validation tests were conducted with Monte Carlo using Geant4 Application for Tomographic Emission for percentage depth dose, in-plane profiles, penumbra, and IMRT dose validation. And the American Association of Physicists in Medicine Task Group 119 benchmarking testing was performed. Additionally, compensator-based cobalt-60 IMRT plans were created for 46 head-and-neck cancer cases and compared to the linac-based volumetric modulated arc therapy (VMAT) plans used clinically, then dosimetric parameters were evaluated. Beam-on time for each field was calculated. In addition, the measurement was also performed in a limited environment and compared with the Monte Carlo simulations. RESULTS: The differences in percent depth doses and in-plane profiles between the Eclipse and Monte Carlo simulations were 0.65% ± 0.41% and 1.02% ± 0.99%, respectively, and the 80%-20% penumbra agreed within 0.46 ± 0.27 mm. For the Task Group 119 validation plans, all treatment planning goals were met and gamma passing rates were >95% (3%/3 mm criteria). In 46 clinical head-and-neck cases, the cobalt-60 compensator-based IMRT plans had planning target volume (PTV) coverages similar to linac-based VMAT plans: all dosimetric values for PTV were within 1.5%. The organs at risk dose parameters were somewhat higher in cobalt-60 compensator-based IMRT plans versus linac-based VMAT plans. The mean dose differences for the spinal cord, brain, and brainstem were 4.43 ± 1.92, 3.39 ± 4.67, and 2.40 ± 3.71 Gy, while those for the rest of the organs were <1 Gy. The average beam-on time per field was 0.42 ± 0.10 min for the 6 MV multi-leaf-collimator plans while those for the cobalt-60 compensator plans were 0.17 ± 0.01 and 0.31 ± 0.01 min at the dose rates of 350 and 175 cGy/min. There was a good agreement between in-plane profiles from measurements and Monte Carlo simulations, which differences are 1.34 ± 1.90% and 0.13 ± 2.16% for two different fields. CONCLUSIONS: A novel compensator-based IMRT system using cobalt-60 beams was commissioned and validated in a commercial TPS. Plan quality with this system was comparable to that of linac-based plans in all test cases with shorter estimated beam-on times. This system enables reliable, high-quality plans with reduced cost and complexity and may have benefits for underserved regions of the world. This system is being integrated into the RPA, a web-based platform for auto-contouring and auto-planning.

Customizable landmark-based field aperture design for automated whole-brain radiotherapy treatment planning.

PURPOSE: To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design. METHODS: The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals. RESULTS: The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients' field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach. The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans. CONCLUSION: This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans.

Apatinib plus chemotherapy versus chemotherapy alone as neoadjuvant therapy in locally advanced gastric carcinoma patients: a prospective, cohort study.

BACKGROUND: Apatinib, a small molecule targeting VEGFR2, is commonly used for advanced gastric cancer treatment. This prospective cohort study further investigated the efficacy and safety of neoadjuvant apatinib plus chemotherapy in locally advanced gastric carcinoma patients. METHODS: Ninety-six locally advanced gastric carcinoma patients were divided into the apatinib plus chemotherapy group (N = 45) and chemotherapy group (N = 51) according to their chosen treatment. Apatinib was administered (375 mg/day), and S-1 plus oxaliplatin (SOX) or oxaliplatin plus capecitabine (CapOx) was given as chemotherapy, for 3 cycles with 3 weeks a cycle before surgery. RESULTS: The objective response rate (62.2% vs. 37.3%, P = 0.015) and pathological response grade (P = 0.011) were better; meanwhile, the tumor-resection rate (95.6% vs. 84.3%, P = 0.143) and pathological complete response rate (23.3% vs. 9.3%, P = 0.080) exhibited increasing trends (without statistical significance) in the apatinib plus chemotherapy group compared with the chemotherapy group. Additionally, the apatinib plus chemotherapy group achieved prolonged disease-free survival (DFS) (P = 0.019) and overall survival (OS) (P = 0.047) compared with the chemotherapy group. After adjusted by multivariate Cox's regression analysis, neoadjuvant apatinib plus chemotherapy was still superior to chemotherapy regarding DFS (hazard ratio (HR): 0.277, P = 0.014) and OS (HR: 0.316, P = 0.038). Notably, the incidences of adverse events between the two groups were not different (P > 0.050). Moreover, the most common adverse events of neoadjuvant apatinib plus chemotherapy were leukopenia (42.2%), fatigue (37.8%), hypertension (37.8%), and anemia (31.1%). CONCLUSION: Neoadjuvant apatinib plus chemotherapy realizes better clinical response, pathological response, survival profile, and non-inferior safety profile compared to chemotherapy in locally advanced gastric carcinoma.

Development and validation of a checklist for use with automatically generated radiotherapy plans.

PURPOSE: To develop a checklist that improves the rate of error detection during the plan review of automatically generated radiotherapy plans. METHODS: A custom checklist was developed using guidance from American Association of Physicists in Medicine task groups 275 and 315 and the results of a failure modes and effects analysis of the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool. The preliminary checklist contained 90 review items for each automatically generated plan. In the first study, eight physicists were recruited from our institution who were familiar with the RPA. Each physicist reviewed 10 artificial intelligence-generated resident treatment plans from the RPA for safety and plan quality, five of which contained errors. Physicists performed plan checks, recorded errors, and rated each plan's clinical acceptability. Following a 2-week break, physicists reviewed 10 additional plans with a similar distribution of errors using our customized checklist. Participants then provided feedback on the usability of the checklist and it was modified accordingly. In a second study, this process was repeated with 14 senior medical physics residents who were randomly assigned to checklist or no checklist for their reviews. Each reviewed 10 plans, five of which contained errors, and completed the corresponding survey. RESULTS: In the first study, the checklist significantly improved the rate of error detection from 3.4 ± 1.1 to 4.4 ± 0.74 errors per participant without and with the checklist, respectively (p = 0.02). Error detection increased by 20% when the custom checklist was utilized. In the second study, 2.9 ± 0.84 and 3.5 ± 0.84 errors per participant were detected without and with the revised checklist, respectively (p = 0.08). Despite the lack of statistical significance for this cohort, error detection increased by 18% when the checklist was utilized. CONCLUSION: Our results indicate that the use of a customized checklist when reviewing automated treatment plans will result in improved patient safety.

Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques.

PURPOSE: To fully automate CT-based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques. METHODS: We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4-field-box (4-field-box), 3D conformal radiotherapy (3D-CRT), and volumetric modulated arc therapy (VMAT). These auto-planning algorithms were combined with a previously developed auto-contouring system. To improve the quality of the 4-field-box and 3D-CRT plans, we used an in-house, field-in-field (FIF) automation program. Thirty-five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5-point Likert scale. RESULTS: Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4-field-box, 3D-CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference. CONCLUSION: Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource-constrained radiotherapy departments in low- and middle-income countries.

Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors.

PURPOSE: Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines. MATERIALS AND METHODS: This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, and planning structures, such that a clinically acceptable plan is achieved in a single optimization. This model was used to generate and optimize volumetric-modulated arc therapy plans for a cohort of 50 patients treated for head-neck cancer. Plans were generated using the following treatment machines: Varian 2100, Elekta Versa HD, and Varian Halcyon. A noninferiority testing methodology was used to evaluate the hypothesis that normal and target metrics in our autoplans were no worse than a set of clinically-acceptable baseline plans by a margin of 1.8 Gy or 3% dose-volume. The quality of these plans were also compared through the use of common clinical dose-volume histogram criteria. RESULTS: The Versa HD met our noninferiority criteria for 23 of 34 normal and target metrics; while the Halcyon and Varian 2100 machines met our criteria for 24 of 34 and 26 of 34 metrics, respectively. The experimental plans tended to have less volume coverage for prescription dose planning target volume and larger hotspot volumes. However, comparable plans were generated across different treatment machines. CONCLUSIONS: These results support the use of a head-neck RapidPlan models in centralized planning workflows that support clinics with different linac models/vendors, although some fine-tuning for targets may be necessary.

Automation of radiation treatment planning for rectal cancer.

PURPOSE: To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward-planning algorithms. METHODS: We designed an algorithm to automate the clinical workflow for 3DCRT planning with field aperture creations and field-in-field (FIF) planning. DL models (DeepLabV3+ architecture) were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary (posterior-anterior [PA] and opposed laterals) and boost fields. Network inputs were digitally reconstructed radiographs, gross tumor volume (GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale (>3 is acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, calculates dose, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with varying wedge angles and definitions of hotspots, and the resulting plans were scored by a physician. The end-to-end workflow was tested and scored by a physician on another 39 patients. RESULTS: The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for PA, laterals, and boost fields, respectively. Overall, 100%, 95%, and 87.5% of the PA, laterals, and boost apertures were scored as clinically acceptable, respectively. At least one auto-plan was clinically acceptable for all patients. Wedged and non-wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The hotspot dose percentage was reduced from 121% (σ = 14%) to 109% (σ = 5%) of prescription dose for all plans. The integrated end-to-end workflow of automatically generated apertures and optimized FIF planning gave clinically acceptable plans for 38/39 (97%) of patients. CONCLUSION: We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.

Automatic contouring QA method using a deep learning-based autocontouring system.

PURPOSE: To determine the most accurate similarity metric when using an independent system to verify automatically generated contours. METHODS: A reference autocontouring system (primary system to create clinical contours) and a verification autocontouring system (secondary system to test the primary contours) were used to generate a pair of 6 female pelvic structures (UteroCervix [uterus + cervix], CTVn [nodal clinical target volume (CTV)], PAN [para-aortic lymph nodes], bladder, rectum, and kidneys) on 49 CT scans from our institution and 38 from other institutions. Additionally, clinically acceptable and unacceptable contours were manually generated using the 49 internal CT scans. Eleven similarity metrics (volumetric Dice similarity coefficient (DSC), Hausdorff distance, 95% Hausdorff distance, mean surface distance, and surface DSC with tolerances from 1 to 10 mm) were calculated between the reference and the verification autocontours, and between the manually generated and the verification autocontours. A support vector machine (SVM) was used to determine the threshold that separates clinically acceptable and unacceptable contours for each structure. The 11 metrics were investigated individually and in certain combinations. Linear, radial basis function, sigmoid, and polynomial kernels were tested using the combinations of metrics as inputs for the SVM. RESULTS: The highest contouring error detection accuracies were 0.91 for the UteroCervix, 0.90 for the CTVn, 0.89 for the PAN, 0.92 for the bladder, 0.95 for the rectum, and 0.97 for the kidneys and were achieved using surface DSCs with a thickness of 1, 2, or 3 mm. The linear kernel was the most accurate and consistent when a combination of metrics was used as an input for the SVM. However, the best model accuracy from the combinations of metrics was not better than the best model accuracy from a surface DSC as an input. CONCLUSIONS: We distinguished clinically acceptable contours from clinically unacceptable contours with an accuracy higher than 0.9 for the targets and critical structures in patients with cervical cancer; the most accurate similarity metric was surface DSC with a thickness of 1, 2, or 3 mm.

6:2 Chlorinated polyfluoroalkyl ether sulfonate as perfluorooctanesulfonate alternative in the electroplating industry and the receiving environment.

Electroplating industry is an important application field of per- and polyfluoroalkyl substances (PFASs) as the chromium mist suppressants. 6:2 chlorinated polyfluoroalkyl ether sulfonate (6:2 Cl-PFAES) and perfluorooctanesulfonate (PFOS) have been the two widely used mist suppressants, and after the ban of PFOS, 6:2 Cl-PFAES will become the dominant suppressant. The behavior and mechanisms of 6:2 Cl-PFAES in the electroplating industry and the receiving environment were studied and compared with PFOS. 6:2 Cl-PFAES behaved similarly with PFOS due to their similar chemical structure. However, some difference exists for the relatively stronger hydrophobicity of 6:2 Cl-PFAES. Up to 35.7 mg/L of PFOS and 13.4 mg/L of 6:2 Cl-PFAES were found in the industrial wastewater influents, and were effectively reduced to 0.3-0.8 mg/L by the interaction with chromium hydroxide through hydrophobic interaction and ligand exchange. The stronger hydrophobicity of 6:2 Cl-PFAES than PFOS resulted in its accumulation in the surface of foams and comparable or less removal during the industrial and municipal wastewater treatment. 6:2 Cl-PFAES exhibited higher bioaccumulation potential than PFOS in the surface water. 6:2 Cl-PFAES emitted by both mists and water may pose health risks to humans. More attentions towards 6:2 Cl-PFAES are needed after the replacement of PFOS by it in the electroplating industry as a global contaminant of emerging concerns.

Clinical Significance of Circulating Cell-Free DNA Detection in Multiple Myeloma: A Meta-Analysis.

Circulating cell-free DNA (cfDNA) detection, a non-invasive method, appears promising for genetic analyses as well as quantitative assessment of tumor burden in patients with cancer. Although the analysis of cfDNA for clinical prognosis and monitoring disease burden in multiple myeloma (MM) has been recently studied, the results are unclear. In this meta-analysis, we explored the clinical significance of circulating cfDNA detection in patients with MM. We searched PubMed, Embase, and the Cochrane Library for eligible studies published up until July 25, 2021. Diagnostic accuracy variables were calculated and analyzed using Meta-Disc, and prognostic data were analyzed using Review Manager. Overall, seven studies comprising 235 myeloma patients met our inclusion criteria. The overall sensitivity and specificity of cfDNA to detect minimal residual disease (MRD) were 0.58 and 0.91, respectively. Moreover, higher levels of cfDNA were associated with worse progression-free survival as well as with poor overall survival. Our meta-analysis revealed that ctDNA detection has an obvious advantage in terms of MRD detection specificity, but it showed no superiority over bone marrow assessment in terms of MRD detection sensitivity, and higher levels of cfDNA were indicative of worse prognosis in patients with MM. cfDNA detection is a non-invasive method and thus shows promise as a good alternative to BM biopsies for monitoring clonal evolution and tumor burden so as to guide the treatment of patients with MM.

Enhanced HSC-like cell generation from mouse pluripotent stem cells in a 3D induction system cocultured with stromal cells.

BACKGROUND: Decades of efforts have attempted to differentiate the pluripotent stem cells (PSCs) into truly functional hematopoietic stem cells (HSCs), yet the problems of low differentiation efficiency in vitro and poor hematopoiesis reconstitution in vivo still exist, mainly attributing to the lack of solid, reproduced, or pursued differentiation system. METHODS: In this study, we established an in vitro differentiation system yielding in vivo hematopoietic reconstitution hematopoietic cells from mouse PSCs through a 3D induction system followed by coculture with OP9 stromal cells. The in vivo hematopoietic reconstitution potential of c-kit+ cells derived from the mouse PSCs was evaluated via m-NSG transplantation assay. Flow cytometry analysis, RNA-seq, and cell cycle analysis were used to detect the in vitro hematopoietic ability of endothelial protein C receptor (EPCR, CD201) cells generated in our induction system. RESULTS: The c-kit+ cells from 3D self-assembling peptide induction system followed by the OP9 coculture system possessed apparently superiority in terms of in vivo repopulating activity than that of 3D induction system followed by the 0.1% gelatin culture. We interestingly found that our 3D+OP9 system enriched a higher percentage of CD201+c-kit+cells that showed more similar HSC-like features such as transcriptome level and CFU formation ability than CD201-c-kit+cells, which have not been reported in the field of mouse PSCs hematopoietic differentiation. Moreover, CD201+ hematopoietic cells remained in a relatively slow cycling state, consistent with high expression levels of P57 and Ccng2. Further, we innovatively demonstrated that notch signaling pathway is responsible for in vitro CD201+ hematopoietic cell induction from mouse PSCs. CONCLUSIONS: Altogether, our findings lay a foundation for improving the efficiency of hematopoietic differentiation and generating in vivo functional HSC-like cells from mouse PSCs for clinical application.

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow.

PURPOSE: We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process. METHODS: We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified. RESULTS: Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans. CONCLUSION: The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.