Abstract. The present study aimed to evaluate the role of adjuvant radiotherapy (RT) following nipple-sparing mastectomy (NSM) for patients with ductal carcinoma in situ and invasive breast cancer, based on the published literature. Currently, there is no standard for RT following NSM. NSM aims to spare the nipple areola complex (NAC) without compromising locoregional control. Long-term follow-up studies have begun to show promising results. A search of the English literature was performed using the Medline database and Cochrane central library, with the keywords 'nipple/areola-sparing mastectomy', 'whole skin mastectomy' and 'NAC preservation'. A total of 32 original studies with data on NSM in terms of locoregional control, NAC control, NAC necrosis and adjuvant RT were identified. The median locoregional and NAC recurrence rates were 3.2 and 1.4% (range, 0-28.4% and 0-3.7%), respectively. The volume of remaining breast tissue following NSM was reported inconsistently. In 15 studies, RT was not mentioned. In the remaining 17 studies, RT was administered in 0-100% of patients. Only 7 studies provided detailed information regarding the use of adjuvant RT. Adjuvant thoracic wall irradiation was not used in certain studies, not even for locoregionally advanced tumors. Overall, NSM appears a feasible treatment without increased risk of locoregional recurrence for selected patients. The role of adjuvant RT following NSM requires further clarification. The decision regarding adjuvant RT must be made in interdisciplinary tumor boards and with consideration of the individual situation of the patient.
PurposeRadiomics has already been proposed as a prognostic biomarker in head and neck cancer (HNSCC). However, its predictive power in radiotherapy has not yet been studied. Here, we investigated a local radiomics approach to distinguish between tumor sub-volumes with different levels of radiosensitivity as a possible target for radiation dose intensification.Materials and MethodsOf 40 patients (n=28 training and n=12 validation) with biopsy confirmed locally recurrent HNSCC, pretreatment contrast-enhanced CT images were registered with follow-up PET/CT imaging allowing identification of controlled (GTVcontrol) vs non-controlled (GTVrec) tumor sub-volumes on pretreatment imaging. A bi-regional model was built using radiomic features extracted from pretreatment CT in the GTVrec and GTVcontrol to differentiate between those regions. Additionally, concept of local radiomics was implemented to perform detection task. The original tumor volume was divided into sub-volumes with no prior information on the location of recurrence. Radiomic features from those sub-volumes were then used to detect recurrent sub-volumes using multivariable logistic regression.ResultsRadiomic features extracted from non-controlled regions differed significantly from those in controlled regions (training AUC = 0.79 CI 95% 0.66 - 0.91 and validation AUC = 0.88 CI 95% 0.72 – 1.00). Local radiomics analysis allowed efficient detection of non-controlled sub-volumes both in the training AUC = 0.66 (CI 95% 0.56 – 0.75) and validation cohort 0.70 (CI 95% 0.53 – 0.86), however performance of this model was inferior to bi-regional model. Both models indicated that sub-volumes characterized by higher heterogeneity were linked to tumor recurrence.ConclusionLocal radiomics is able to detect sub-volumes with decreased radiosensitivity, associated with location of tumor recurrence in HNSCC in the pre-treatment CT imaging. This proof of concept study, indicates that local CT radiomics can be used as predictive biomarker in radiotherapy and potential target for dose intensification.
Zusammenfassung. Die Radiotherapie hat einen Stellenwert im Therapiealgorithmus auch bei nicht-malignen, benignen Erkrankungen der Gelenke und des Bindegewebes. Eine grosse Anzahl an degenerativen, entzündlichen, hyperproliferativen und funktionellen Erkrankungen kann mit gutem Erfolg durch eine Bestrahlung behandelt werden. Dabei ist diese Therapieform für den Patienten belastungs- und nebenwirkungsarm.
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