Whole breast ultrafractionation radiotherapy after breast-conserving surgery in early breast cancer: A single-center, prospective, observational study from China

Objective:This single-center,prospective,observational study was designed to investigate the toxicities,patient-reported outcome(PRO),and dosimetric analysis of whole breast ultrafractionation radiotherapy(RT)after breast-conserving surgery(BCS)in early breast cancer(BC).Patients and methods:Patients diagnosed with BC stage I,II and treated with BCS were enrolled.A dose of 26 Gray(Gy)in five fractions was prescribed to the whole breast and tumor bed.Clinical endpoints included toxicities,PRO,and dosimetric analysis.PRO was measured by the European Organization for Research and Treatment of Cancer general quality of life questionnaire(EORTC QLQ-C30)and the BC-specific questionnaire(EORTC QLQ-BR23)questionnaires.Results:Between January 2022 and June 2023,62 female patients were enrolled.The median age was 45 years.Most patients(83.9%)were diagnosed with pathological stage I disease.The median planning target volume(PTV)was 456.4 mL.The minimum,maximum,and mean doses,and D95(dose of PTV irradiated volume more than 95%)to PTV were 20.2,28.8,27.2,and 26.3 Gy,respectively.The median mean lung dose and percentage lung volume receiving 8 Gy(V8)were 3.6 Gy and 13.4%,respectively.The median mean heart dose,V1.5(percentage of organ volume irradiated with 1.5 Gy or higher),and V7(percentage of organ volume irradiated with 7 Gy or higher)were 0.6 Gy,6.8%,and 0.4%,respectively.Cosmetic effects before RT showed no obvious differences compared to that post RT.No toxicities of grade 3 or higher occurred.Five patients had asymptomatic radiation pneumonia(grade 1),and 12 patients had radiation dermatitis(grade 1).No factor was significantly related to radiation dermatitis or radiation pneumonia.For the EORTC QLQ-C30 and QLQ-BR23 questionnaires,all function and symptom scores before RT had no significant differences compared with that after RT,1−2 months after RT,and 3−4 months after RT.Ultrafractionation RT did not worsen PRO.The 1-year crude local control was 100%.Conclusion:Whole breast ultrafractionation RT after BCS in early BC has no severe toxicities and does not affect PRO.These results need to be further validated with a longer follow-up and a larger sample size.

Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer

Background:Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features.This study aimed to use deep neural netAVorks for computed tomography(CT)diagnosis of perigastric metastatic lymph nodes(PGMLNs)to simulate the recognition of lymph nodes by radiologists,and to acquire more accurate identification results.Methods:A total of 1371 images of suspected lymph node metastasis from enhanced abdominal CT scans were identified and labeled by radiologists and were used with 18,780 original images for faster region-based convolutional neural networks(FR-CNN)deep learning.The identification results of 6000 random CT images from 100 gastric cancer patients by the FR-CNN were compared with results obtained from radiologists in terms of their identification accuracy.Similarly,1004 CT images with metastatic lymph nodes that had been post-operatively confirmed by pathological examination and 11,340 original images were used in the identification and learning processes described above.The same 6000 gastric cancer CT images were used for the verification,according to which the diagnosis results were analyzed.Results:In the initial group,precision-recall curves were generated based on the precision rates,the recall rates of nodule classes of the training set and the validation set;the mean average precision(mAP)value was 0.5019.To verify the results of the initial learning group,the receiver operating characteristic curves was generated,and the corresponding area under the curve(AUC)value was calculated as 0.8995.After the second phase of precise learning,all the indicators were improved,and the mAP and AUC values were 0.7801 and 0.9541,respectively.Conclusion:Through deep learning,FR-CNN achieved high judgment effectiveness and recognition accuracy for CT diagnosis of PGMLNs.