Simultaneous integrated boost within the lymphatic drainage system in breast cancer: A single center study on toxicity and oncologic outcome

Background and purposeIn breast cancer patients, the increasing de-escalation of axillary surgery and the improving resolution of diagnostic imaging results in a more frequent detection of residual, radiographically suspect lymph nodes (sLN) after surgery. If resection of the remaining suspect lymph nodes is not feasible, a simultaneous boost to the lymph node metastases (LN-SIB) can be applied. However, literature lacks data regarding the outcome and safety of this technique.Materials and methodsWe included 48 patients with breast cancer and sLN in this retrospective study. All patients received a LN-SIB. The median dose to the breast or chest wall and the lymph node system was 50.4 Gy in 28 fractions. The median dose of the LN-SIB was 58.8 Gy / 2.1 Gy (56-63 Gy / 2-2.25 Gy). The brachial plexus was contoured in every case and the dose within the plexus PRV (+0.3-0.5mm) was limited to an EQD2 of 59 Gy. All patients received structured radiooncological and gynecological follow-up by clinically experienced physicians. Radiooncological follow-ups were at baseline, 6 weeks, 3 months, 6 months and subsequent annually after irradiation.ResultsThe median follow-up time was 557 days and ranged from 41 to 3373 days. Overall, 28 patients developed I°, 18 patients II° and 2 patients III° acute toxicity. There were no severe late side effects (≥ III°) observed during the follow-up period. The most frequent chronic side effect was fatigue. One patient (2.1 %) developed pain and mild paresthesia in the ipsilateral arm after radiotherapy. After a follow-up of 557 days (41 to 3373 days), in 8 patients a recurrence was observed (16.7%). In 4 patients the recurrence involved the regional lymph node system. Hence, local control in the lymph node drainage system after a median follow-up of 557 days was 91.6 %.ConclusionIf surgical re-dissection of residual lymph nodes is not feasible or refused by the patient, LN-SIB-irradiation can be considered as a potential treatment option. However, patients need to be informed about a higher risk of regional recurrence compared to surgery and an additional risk of acute and late toxicity compared to adjuvant radiotherapy without regional dose escalation

Use of acupuncture to alleviate side effects in radiation oncology: Current evidence and future directions

Several reports have shown that acupuncture is an effective method of complementary medicine; however, only a few of these reports have focused on oncological patients treated with radiation therapy. Most of these studies discuss a benefit of acupuncture for side-effect reduction; however, not all could demonstrate significant improvements. Thus, innovative trial designs are necessary to confirm that acupuncture can alleviate side effects related to radiation therapy. In the present manuscript, we perform a broad review and discuss pitfalls and limitations of acupuncture in parallel with standard radiation therapy, which lead the way to novel treatment concepts

Additional file 1: of Randomized study exploring the combination of radiotherapy with two types of acupuncture treatment (ROSETTA): study protocol for a randomized controlled trial

SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents. (DOC 110 kb

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation