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

Background and purpose: In 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 methods: We 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. Results: The 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 %. Conclusion: If 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.

As Easy as 1, 2, 3? How to Determine CBCT Frequency in Adjuvant Breast Radiotherapy.

The current study aims to assess the suitability of setup errors during the first three treatment fractions to determine cone-beam computed tomography (CBCT) frequency in adjuvant breast radiotherapy. For this, 45 breast cancer patients receiving non-hypofractionated radiotherapy after lumpectomy, including a simultaneous integrated boost (SIB) to the tumor bed and daily CBCT imaging, were retrospectively selected. In a first step, mean and maximum setup errors on treatment days 1-3 were correlated with the mean setup errors during subsequent treatment days. In a second step, dose distribution was estimated using a dose accumulation workflow based on deformable image registration, and setup errors on treatment days 1-3 were correlated with dose deviations in the clinical target volumes (CTV) and organs at risk (OAR). No significant correlation was found between mean and maximum setup errors on treatment days 1-3 and mean setup errors during subsequent treatment days. In addition, mean and maximum setup errors on treatment days 1-3 correlated poorly with dose coverage of the CTVs and dose to the OARs. Thus, CBCT frequency in adjuvant breast radiotherapy should not be determined solely based on the magnitude of setup errors during the first three treatment fractions.

MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.

PURPOSE: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR). METHODS: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort. RESULTS: The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression. CONCLUSION: This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.

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.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.

BACKGROUND: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). METHODS: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. RESULTS: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. CONCLUSIONS: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Complementary and Alternative Medicine in Radiotherapy: A Comprehensive Review.

Complementary and alternative medicine (CAM) approaches are widely used by patients throughout a broad range of medical fields and diseases, and often self-administered by patients without the involvement of physicians or other members of the health care team. CAM use is well documented in cancer and chronic illnesses, and emerging data in radiation oncology show CAM usage of 26% to 97% in radiation therapy patients. No information is, however, available on CAM usage in radiology and in the imaging procedure fields. This article reviews the fundamental principles and the experience with the wide spectrum of CAM in radiation oncology-a field that shares many parallels with radiology, such as prevalence of imaging, procedural requirements, and cooperation demanded from patients.CAM is defined as "approaches and practices that are typically not part of conventional medical care," and includes the use of mind- and body-based practices (eg, meditation, massage, acupuncture), natural products (eg, herbs, vitamins, minerals), and other interventions. Supplements are used frequently to alleviate side effects of therapy and promote overall well-being. Specifically, the mindfulness/meditation approaches of CAM are known to reduce anxiety and enhance physical and emotional wellbeing in patients with chronic diseases, such as cancer or neurologic diseases, through physiological, psychological, and perhaps placebo mechanisms. Such patients often require repetitive and invasive imaging examinations or procedures, such as for cancer treatment, cancer surveillance/follow-up, or monitoring of chronic diseases, for example, surveillance MRI in multiple sclerosis. Such parallels suggest that the vastly understudied area of CAMs deserve further investigation in both the radiation oncology and the imaging fields. Further research on CAM is needed to develop refined recommendations and national/and international guidelines on its use.

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

BACKGROUND: Adverse effects such as fatigue, pain, erythema, nausea and vomiting are commonly known in patients undergoing irradiation (RT) alone or in combination with chemotherapy (RCHT). Patients suffering from these symptoms are limited in their daily life and their quality of life (QOL) is often reduced. As addressed in several trials, acupuncture can cause amelioration of these specific disorders. Especially for pain symptoms, several groups have shown efficacy of acupuncture. To what extent the difference between traditional acupuncture (verum acupuncture) and false acupuncture (sham acupuncture) is in reducing side effects and improvement of QOL is not clear. METHODS/DESIGN: ROSETTA is a prospective randomized phase II trial (version 1.0) to examine the efficacy of traditional acupuncture in patients with RT-related side effects. In the experimental (verum) arm (n = 37) an experienced acupuncture-trained person will treat dedicated acupuncture points. In the control (sham) arm (n = 37) sham acupuncture will be performed to provide a blinded comparison of results. DISCUSSION: This is the first randomized prospective trial to evaluate the effect of traditional acupuncture on RT-related side effects such as fatigue and QOL. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02674646 . Registered on 8 December 2015.

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.