Desmoid Tumors in Familial Adenomatous Polyposis Patients: Favorable Outcomes with Multidisciplinary Management.

OBJECTIVE: In this study, we aimed to describe the clinical features, management, and outcomes of desmoid tumors (DTs) in familial adenomatous polyposis (FAP) patients at a high-volume sarcoma center. METHODS: Consecutive patients with FAP and DTs were identified from our institutional databases (1985-2021). Patient demographics, treatment, and outcomes were described. Categorical data were compared using Fisher's exact test, and Kaplan-Meier curves were used to estimate progression-free survival (PFS). RESULTS: Forty-five patients with 67 DTs were identified: 39 mesenteric or retroperitoneal (58.2%), 17 abdominal wall (25.4%), 4 extremity (6%), 4 breast (6%) and 3 back (4.4%). Severe DT symptoms were present in 12 patients (26.7%). Initial treatments per tumor were observation in 30 (44.8%) DTs, chemotherapy in 15 (22.4%) DTs, surgery in 10 (14.9%) DTs, and other systemic therapies in 10 (14.9%) DTs. The majority of DTs remained stable with observation or a single intervention (77.8%). Median PFS was 23.4 years (95% confidence interval 7.6-39.2). In the 12 severely symptomatic patients, four patients required more than two interventions for DT control. At a median follow-up of 6.0 years (range 0.7-35.8 years), 33 (73.3%) patients were alive with disease, 7 (15.6%) were alive without disease, and 5 (11.1%) died of other causes. No patients died of DT-related complications. CONCLUSIONS: The majority of DTs in FAP patients remained stable with observation or a single intervention. There were no DT-related deaths; however, 12 of 45 patients (26.7%) experienced significant tumor morbidity and required more interventions for disease control. Further studies on quality of life are required.

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

BACKGROUND: Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer. MATERIALS AND METHODS: MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance. RESULTS: 7 features were significantly (p < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively. CONCLUSIONS: Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.

Morbidity and Outcomes After Distal Pancreatectomy for Primary Retroperitoneal Sarcoma: An Analysis by the Trans-Atlantic Australasian Retroperitoneal Sarcoma Working Group.

BACKGROUND: Multi-visceral resection often is used in the treatment of retroperitoneal sarcoma (RPS). The morbidity after distal pancreatectomy for primary pancreatic cancer is well-documented, but the outcomes after distal pancreatectomy for primary RPS are not. This study aimed to evaluate morbidity and oncologic outcomes after distal pancreatectomy for primary RPS. METHODS: In this study, 26 sarcoma centers that are members of the Trans-Atlantic Australasian Retroperitoneal Sarcoma Working Group (TARPSWG) retrospectively identified consecutive patients who underwent distal pancreatectomy for primary RPS from 2008 to 2017. The outcomes measured were 90-day severe complications (Clavien-Dindo ≥ 3), postoperative pancreatic fistula (POPF) rate, and oncologic outcomes. RESULTS: Between 2008 and 2017, 280 patients underwent distal pancreatectomy for primary RPS. The median tumor size was 25 cm, and the median number of organs resected, including the pancreas, was three. In 96% of the operations, R0/R1 resection was achieved. The 90-day severe complication rate was 40 %. The grades B and C POPF complication rates were respectively 19% and 5% and not associated with worse overall survival. Administration of preoperative radiation and factors to mitigate POPF did not have an impact on the risk for the development of a POPF. The RPS invaded the pancreas in 38% of the patients, and local recurrence was doubled for the patients who had a microscopic, positive pancreas margin (hazard ratio, 2.0; p = 0.042). CONCLUSION: Distal pancreatectomy for primary RPS has acceptable morbidity and oncologic outcomes and is a reasonable approach to facilitate complete tumor resection.

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies.

AIM: We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). MATERIALS & METHODS: QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders. RESULTS: Single-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively. CONCLUSION: QUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment.

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer.

AIM: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. MATERIALS & METHODS: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. RESULTS: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. CONCLUSION: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps.

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.