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Background Local recurrences after breast conserving treatment are mainly close to the original tumor site, and as such shorter fractionation strategies focused on and nearest mammary gland, i.e. accelerated partial breast irradiation (APBI), have been developed. Stereotactic APBI has been attempted, although there is little experience using CyberKnife (CK) for early breast cancer. Methods This pilot study was designed to assess the feasibility of CK-APBI on 20 evaluable patients of 29 eligible, followed for 2 years. The primary endpoint was acute/sub-acute toxicity; secondary endpoints were late toxicity and the cosmetic result. Results Mean pathological tumor size was 10.5 mm (±4.3, range 3–18), 8 of these patients were classified as LumA-like, 11 as LumB-like, and 1 as LumB-HER2-enriched. Using CK-APBI with Iris, the treatment time was approximately 60 min (range~ 35 to ~ 120). All patients received 30 Gy in five fractions delivered to the PTV. The median number of beams was 180 (IQR 107–213; range:56–325) with a median PTV isodose prescription of 86.0% (IQR 85.0–88.5; range:82–94). The median PTV was 88.1 cm3 (IQR 63.8–108.6; range:32.3–238.8). The median breast V100 and V50 was 0.6 (IQR 0.1–1.5; range:0–13) and 18.6 (IQR 13.1–21.7; range:7.5–37), respectively. The median PTV minimum dose was 26.2 Gy (IQR 24.7–27.6; range 22.3–29.3). Mild side effects were recorded during the period of observation. Cosmetic evaluations were performed by three observers from the start of radiotherapy up to 2 years. Patients’ evaluation progressively increase from 60% to 85% of excellent rating; this trend was similar to that of external observer. Conclusions These preliminary results showed the safe feasibility of CK-APBI in early breast cancer, with mild acute and late toxicity and very good cosmetic results. Trial registration The present study is registered at Clinicaltrial.gov ( NCT02896322 ). Retrospectively egistered August 4, 2016.

Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile. Methods: A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2−). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (α = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics. Results: Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842. Conclusions: Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice.

In women at high/intermediate lifetime risk of breast cancer (BC-LTR), contrast-enhanced magnetic resonance imaging (MRI) added to mammography ± ultrasound (MX ± US) increases sensitivity but decreases specificity. Screening with MRI alone is an alternative and potentially more cost-effective strategy. Here, we describe the study protocol and the characteristics of enrolled patients for MRIB feasibility, multicenter, randomized, controlled trial, which aims to compare MRI alone versus MX+US in women at intermediate breast cancer risk (aged 40–59, with a 15–30% BC-LTR and/or extremely dense breasts). Two screening rounds per woman were planned in ten centers experienced in MRI screening, the primary endpoint being the rate of cancers detected in the 2 arms after 5 years of follow-up. From July 2013 to November 2015, 1254 women (mean age 47 years) were enrolled: 624 were assigned to MX+US and 630 to MRI. Most of them were aged below 50 (72%) and premenopausal (45%), and 52% used oral contraceptives. Among postmenopausal women, 15% had used hormone replacement therapy. Breast and/or ovarian cancer in mothers and/or sisters were reported by 37% of enrolled women, 79% had extremely dense breasts, and 41% had a 15–30% BC-LTR. The distribution of the major determinants of breast cancer risk profiles (breast density and family history of breast and ovarian cancer) of enrolled women varied across centers.

Patients with diffuse pontine gliomas have a median survival of less than one year and represent a challenge for pediatric oncologists, prompting them to attempt experimental therapies. From 1987 to 2005, 62 children with diffuse pontine glioma, not amenable to curative surgery, were treated according to four successive pilot protocols: (1) concomitant chemo–radiotherapy (etoposide, cytarabine, ifosfamide, cisplatin, and dactinomycin); (2) intensive high-dose courses chemotherapy (cisplatin/etoposide, cyclophosphamide/vincristine/methotrexate) and a subsequent course of myeloablative thiotepa followed by radiation and maintenance chemotherapy; (3) cisplatin/etoposide followed by isotretinoin before, during and after focal irradiation; and (4) iv vinorelbine before, during, and after irradiation. Considering all patients, 77% experienced a transient response to treatment, always detectable after radiotherapy. The progression-free survival (PFS) rate was 25 ± 6% at one year, median PFS was seven months; overall survival (OS) was 45 ± 6%, median OS was eleven months: no statistical differences in the four studies in terms of outcome were detected. Despite improved diagnostic, therapeutic, and supportive tools in pediatric neuro-oncology, little has been achieved for patients with diffuse pontine tumors.

We evaluated whether (18)F-3'-deoxy-3'-fluorothymidine positron emission tomography (FLT PET) can predict the final postoperative histopathological response in primary breast cancer after the first cycle of neoadjuvant chemotherapy (NCT). In this prospective cohort study of 15 patients with locally advanced operable breast cancer, FLT PET evaluations were performed before NCT, after the first cycle of NCT, and at the end of NCT. All patients subsequently underwent surgery. Variables from FLT PET examinations were correlated with postoperative histopathological results. At baseline, median of maximum standardized uptake values (SUVmax) in the groups showing a complete pathological response (pCR) + residual cancer burden (RCB) I, RCB II or RCB III did not differ significantly for the primary tumour (5.0 vs. 2.9 vs. 8.9, p = 0.293) or for axillary nodes (7.9 vs. 1.6 vs. 7.0, p = 0.363), whereas the Spearman correlation between SUVmax and Ki67 proliferation rate index was significant (r = 0.69, p < 0.001). Analysis of the relative percentage change of SUVmaxin the primary tumour (∆SUVTmax(t₁)) and axillary nodes (∆SUVNmax(t₁)) after the first NCT cycle showed that the power of ∆SUVTmax(t 1) to predict pCR + RCB I responses (AUC = 0.91, p < 0.001) was statistically significant, whereas ∆SUVNmax(t₁) had a moderate ability (AUC = 0.77, p = 0.119) to separate subjects with ΔSUVTmax(t₁) > -52.9 % into two groups: RCB III patients and a heterogeneous group that included RCB I and RCB II patients. A predictive score μ based on ΔSUVTmax(t₁) and ΔSUVNmax(t₁) parameters is proposed. The preliminary findings of the present study suggest the potential utility of FLT PET scans for early monitoring of response to NCT and to formulate a therapeutic strategy consistent with the estimated efficacy of NCT. However, these results in a small patient population need to be validated in a larger independent cohort.

Breast cancer screening and presurgical diagnosis are currently based on mammography, ultrasound and more sensitive imaging technologies; however, noninvasive biomarkers represent both a challenge and an opportunity for early detection of cancer. An extensive number of potential breast cancer biomarkers have been discovered by microarray hybridization or sequencing of circulating DNA, noncoding RNA and blood cell RNA; multiplex analysis of immune-related molecules and mass spectrometry-based approaches for high-throughput detection of protein, endogenous peptides, circulating and volatile metabolites. However, their medical relevance and their translation to clinics remain to be exploited. Once they will be fully validated, cancer biomarkers, used in combination with the current and emerging imaging technologies, represent an avenue to a personalized breast cancer diagnosis.

Purpose: We evaluated whether super(18)F-3'-deoxy-3'-fluorothym idine positron emission tomography (FLT PET) can predict the final postoperative histopathological response in primary breast cancer after the first cycle of neoadjuvant chemotherapy (NCT). Methods: In this prospective cohort study of 15 patients with locally advanced operable breast cancer, FLT PET evaluations were performed before NCT, after the first cycle of NCT, and at the end of NCT. All patients subsequently underwent surgery. Variables from FLT PET examinations were correlated with postoperative histopathological results. Results: At baseline, median of maximum standardized uptake values (SUV sub(max)) in the groups showing a complete pathological response (pCR) + residual cancer burden (RCB) I, RCB II or RCB III did not differ significantly for the primary tumour (5.0 vs. 2.9 vs. 8.9, p=0.293) or for axillary nodes (7.9 vs. 1.6 vs. 7.0, p=0.363), whereas the Spearman correlation between SUV sub(max) and Ki67 proliferation rate index was significant (r=0.69, p<0.001). Analysis of the relative percentage change of SUV sub(max)in the primary tumour ( Delta SUVT sub(max)(t sub(1))) and axillary nodes ( Delta SUVN sub(max)(t sub(1))) after the first NCT cycle showed that the power of Delta SUVT sub(max)(t sub(1)) to predict pCR + RCB I responses (AUC=0.91, p<0.001) was statistically significant, whereas Delta SUVN sub(max)(t sub(1)) had a moderate ability (AUC=0.77, p=0.119) to separate subjects with Delta SUVT sub(max)(t sub(1))>-52.9% into two groups: RCB III patients and a heterogeneous group that included RCB I and RCB II patients. A predictive score mu based on Delta SUVT sub(max)(t sub(1)) and Delta SUVN sub(max)(t sub(1)) parameters is proposed. Conclusion: The preliminary findings of the present study suggest the potential utility of FLT PET scans for early monitoring of response to NCT and to formulate a therapeutic strategy consistent with the estimated efficacy of NCT. However, these results in a small patient population need to be validated in a larger independent cohort.

We evaluated whether ^sup 18^F-3'-deoxy-3'-fluorothymidine positron emission tomography (FLT PET) can predict the final postoperative histopathological response in primary breast cancer after the first cycle of neoadjuvant chemotherapy (NCT). In this prospective cohort study of 15 patients with locally advanced operable breast cancer, FLT PET evaluations were performed before NCT, after the first cycle of NCT, and at the end of NCT. All patients subsequently underwent surgery. Variables from FLT PET examinations were correlated with postoperative histopathological results. At baseline, median of maximum standardized uptake values (SUV^sub max^) in the groups showing a complete pathological response (pCR) + residual cancer burden (RCB) I, RCB II or RCB III did not differ significantly for the primary tumour (5.0 vs. 2.9 vs. 8.9, p=0.293) or for axillary nodes (7.9 vs. 1.6 vs. 7.0, p=0.363), whereas the Spearman correlation between SUV^sub max^ and Ki67 proliferation rate index was significant (r=0.69, p<0.001). Analysis of the relative percentage change of SUV^sub max^in the primary tumour ([increment]SUVT^sub max^(t ^sub 1^)) and axillary nodes ([increment]SUVN^sub max^(t ^sub 1^)) after the first NCT cycle showed that the power of [increment]SUVT^sub max^(t ^sub 1^) to predict pCR + RCB I responses (AUC=0.91, p<0.001) was statistically significant, whereas [increment]SUVN^sub max^(t ^sub 1^) had a moderate ability (AUC=0.77, p=0.119) to separate subjects with [Delta]SUVT^sub max^(t ^sub 1^)>-52.9% into two groups: RCB III patients and a heterogeneous group that included RCB I and RCB II patients. A predictive score [mu] based on [Delta]SUVT^sub max^(t ^sub 1^) and [Delta]SUVN^sub max^(t ^sub 1^) parameters is proposed. The preliminary findings of the present study suggest the potential utility of FLT PET scans for early monitoring of response to NCT and to formulate a therapeutic strategy consistent with the estimated efficacy of NCT. However, these results in a small patient population need to be validated in a larger independent cohort.

Purpose We evaluated whether 18 F-3′-deoxy-3′-fluorothymidine positron emission tomography (FLT PET) can predict the final postoperative histopathological response in primary breast cancer after the first cycle of neoadjuvant chemotherapy (NCT). Methods In this prospective cohort study of 15 patients with locally advanced operable breast cancer, FLT PET evaluations were performed before NCT, after the first cycle of NCT, and at the end of NCT. All patients subsequently underwent surgery. Variables from FLT PET examinations were correlated with postoperative histopathological results. Results At baseline, median of maximum standardized uptake values (SUV max ) in the groups showing a complete pathological response (pCR) + residual cancer burden (RCB) I, RCB II or RCB III did not differ significantly for the primary tumour (5.0 vs. 2.9 vs. 8.9, p  = 0.293) or for axillary nodes (7.9 vs. 1.6 vs. 7.0, p  = 0.363), whereas the Spearman correlation between SUV max and Ki67 proliferation rate index was significant ( r  = 0.69, p  < 0.001). Analysis of the relative percentage change of SUV max in the primary tumour (∆SUVT max ( t 1 )) and axillary nodes (∆SUVN max ( t 1 )) after the first NCT cycle showed that the power of ∆SUVT max ( t 1 ) to predict pCR + RCB I responses (AUC = 0.91, p  < 0.001) was statistically significant, whereas ∆SUVN max ( t 1 ) had a moderate ability (AUC = 0.77, p  = 0.119) to separate subjects with ΔSUVT max ( t 1 ) > −52.9 % into two groups: RCB III patients and a heterogeneous group that included RCB I and RCB II patients. A predictive score μ based on ΔSUVT max ( t 1 ) and ΔSUVN max ( t 1 ) parameters is proposed. Conclusion The preliminary findings of the present study suggest the potential utility of FLT PET scans for early monitoring of response to NCT and to formulate a therapeutic strategy consistent with the estimated efficacy of NCT. However, these results in a small patient population need to be validated in a larger independent cohort.