Breast cancer in multiple endocrine neoplasia type 1 (MEN1).

SUMMARY: A 38-year-old female was identified as carrying a heterozygous pathogenic MEN1 variant (c.1304delG) through predictive genetic testing, following a diagnosis of familial hyperparathyroidism. Routine screening for parathyroid and pituitary disease was negative. However, cross-sectional imaging by CT revealed a 41 mm pancreatic tail mass. Biopsy via endoscopic ultrasound confirmed the lesion to be a well-differentiated (grade 1) pancreatic neuroendocrine tumour (pNET) with MIB1<1%. Biochemically, hyperinsulinaemic hypoglycaemia was confirmed following an overnight fast, which was subsequently managed by diet alone prior to definitive surgery. Pre-operative work-up with octreotide SPECT CT demonstrated avid tracer uptake in the pancreatic lesion and, unexpectedly, a focal area of uptake in the left breast. Further investigation, and subsequent mastectomy, confirmed ductal carcinoma in situ pT2 (23 mm) grade 1, N0 (ER positive; HER2 negative). Following mastectomy, our patient underwent a successful distal pancreatectomy to resect the pNET. Loss of heterozygosity (LOH) at the MEN1 locus was found in both the breast tumour and pNET, thereby in keeping with a 'two-hit' hypothesis of oncogenesis, a suggestive but non-definitive clue for causation. To obtain further support for a causative relationship between MEN1 and breast cancer, we undertook a detailed review of the published literature which overall supports the notion that breast cancer is a MEN1-related malignancy that presents at a younger age and histologically, is typically of ductal subtype. Currently, clinical guidance regarding breast cancer surveillance in MEN1 does not exist and further research is required to establish a clinical and cost-effective surveillance strategy). LEARNING POINTS: We describe a case of pNET and breast cancer diagnosed at a young age of 38 years in a patient who is heterozygous for a pathogenic MEN1 variant. Loss of the wild-type allele was seen in both breast tissue and pNET specimen. Breast cancer may be an under-recognised MEN1-associated malignancy that presents at a younger age than in the general population with a relative risk of 2-3. Further research is required to determine the cost-effectiveness of breast cancer surveillance approach at a younger age in MEN1 patients relative to the general population .

Prediction of lymph node metastases using pre-treatment PET radiomics of the primary tumour in esophageal adenocarcinoma: an external validation study.

OBJECTIVES: To improve clinical lymph node staging (cN-stage) in oesophageal adenocarcinoma by developing and externally validating three prediction models; one with clinical variables only, one with positron emission tomography (PET) radiomics only, and a combined clinical and radiomics model. METHODS: Consecutive patients with fluorodeoxyglucose (FDG) avid tumours treated with neoadjuvant therapy between 2010 and 2016 in two international centres (n = 130 and n = 60, respectively) were included. Four clinical variables (age, gender, clinical T-stage and tumour regression grade) and PET radiomics from the primary tumour were used for model development. Diagnostic accuracy, area under curve (AUC), discrimination and calibration were calculated for each model. The prognostic significance was also assessed. RESULTS: The incidence of lymph node metastases was 58% in both cohorts. The areas under the curve of the clinical, radiomics and combined models were 0.79, 0.69 and 0.82 in the developmental cohort, and 0.65, 0.63 and 0.69 in the external validation cohort, with good calibration demonstrated. The area under the curve of current cN-stage in development and validation cohorts was 0.60 and 0.66, respectively. For overall survival, the combined clinical and radiomics model achieved the best discrimination performance in the external validation cohort (X2 = 6.08, df = 1, p = 0.01). CONCLUSION: Accurate diagnosis of lymph node metastases is crucial for prognosis and guiding treatment decisions. Despite finding improved predictive performance in the development cohort, the models using PET radiomics derived from the primary tumour were not fully replicated in an external validation cohort. ADVANCES IN KNOWLEDGE: This international study attempted to externally validate a new prediction model for lymph node metastases using PET radiomics. A model combining clinical variables and PET radiomics improved discrimination of lymph node metastases, but these results were not externally replicated.

Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging.

Radiomic studies link quantitative imaging features to patient outcomes in an effort to personalise treatment in oncology. To be clinically useful, a radiomic feature must be robust to image processing steps, which has made robustness testing a necessity for many technical aspects of feature extraction. We assessed the stability of radiomic features to interpolation processing and categorised features based on stable, systematic, or unstable responses. Here, 18F-fluorodeoxyglucose (18F-FDG) PET images for 441 oesophageal cancer patients (split: testing = 353, validation = 88) were resampled to 6 isotropic voxel sizes (1.5 mm, 1.8 mm, 2.0 mm, 2.2 mm, 2.5 mm, 2.7 mm) and 141 features were extracted from each volume of interest (VOI). Features were categorised into four groups with two statistical tests. Feature reliability was analysed using an intraclass correlation coefficient (ICC) and patient ranking consistency was assessed using a Spearman's rank correlation coefficient (ρ). We categorised 93 features robust and 6 limited robustness (stable responses), 34 potentially correctable (systematic responses), and 8 not robust (unstable responses). We developed a correction technique for features with potential systematic variation that used surface fits to link voxel size and percentage change in feature value. Twenty-nine potentially correctable features were re-categorised to robust for the validation dataset, after applying corrections defined by surface fits generated on the testing dataset. Furthermore, we found the choice of interpolation algorithm alone (spline vs trilinear) resulted in large variation in values for a number of features but the response categorisations remained constant. This study attempted to quantify the diverse response of radiomics features commonly found in 18F-FDG PET clinical modelling to isotropic voxel size interpolation.

Machine-learned target volume delineation of 18F-FDG PET images after one cycle of induction chemotherapy.

Biological tumour volume (GTVPET) delineation on 18F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTVPET. Automatic segmentation algorithms applied to 18F-FDG PET (PET-AS) imaging have been used for GTVPET delineation on 18F-FDG PET imaging acquired before ICT. However, their role has not been investigated in 18F-FDG PET imaging acquired after ICT. In this study we investigate PET-AS techniques, including ATLAAS a machine learned method, for accurate delineation of the GTVPET after ICT. Twenty patients were enrolled onto a prospective phase I study (FiGaRO). PET/CT imaging was acquired at baseline and 3 weeks following 1 cycle of induction chemotherapy. The GTVPET was manually delineated by a nuclear medicine physician and clinical oncologist. The resulting GTVPET was used as the reference contour. The ATLAAS original statistical model was expanded to include images of reduced metabolic activity and the ATLAAS algorithm was re-trained on the new reference dataset. Estimated GTVPET contours were derived using sixteen PET-AS methods and compared to the GTVPET using the Dice Similarity Coefficient (DSC). The mean DSC for ATLAAS, 60% Peak Thresholding (PT60), Adaptive Thresholding (AT) and Watershed Thresholding (WT) was 0.72, 0.61, 0.63 and 0.60 respectively. The GTVPET generated by ATLAAS compared favourably with manually delineated volumes and in comparison, to other PET-AS methods, was more accurate for GTVPET delineation after ICT. ATLAAS would be a feasible method to reduce inter-observer variability in multi-centre trials.

External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer.

AIM: Enhanced prognostic models are required to improve risk stratification of patients with oesophageal cancer so treatment decisions can be optimised. The primary aim was to externally validate a published prognostic model incorporating PET image features. Transferability of the model was compared using only clinical variables. METHODS: This was a Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis (TRIPOD) type 3 study. The model was validated against patients treated with neoadjuvant chemoradiotherapy according to the Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS) trial regimen using pre- and post-harmonised image features. The Kaplan-Meier method with log-rank significance tests assessed risk strata discrimination. A Cox proportional hazards model assessed model calibration. Primary outcome was overall survival (OS). RESULTS: Between 2010 and 2015, 449 patients were included in the development (n = 302), internal validation (n = 101) and external validation (n = 46) cohorts. No statistically significant difference in OS between patient quartiles was demonstrated in prognostic models incorporating PET image features (X2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X2 = 1.19, df = 3, p = 0.75). The calibration slope ß of both models was not significantly different from unity (p = 0.29 and 0.29, respectively). Risk groups defined using only clinical variables suggested differences in OS, although these were not statistically significant (X2 = 0.71, df = 2, p = 0.70). CONCLUSION: The prognostic model did not enable significant discrimination between the validation risk groups, but a second model with exclusively clinical variables suggested some transferable prognostic ability. PET harmonisation did not significantly change the results of model validation.

Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods.

BACKGROUND: Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. RESULTS: Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. CONCLUSION: Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer.

OBJECTIVES: This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the model was performed. METHODS: Consecutive OC patients (n = 403) were chronologically separated into development (n = 302, September 2010-September 2014, median age = 67.0, males = 227, adenocarcinomas = 237) and validation cohorts (n = 101, September 2014-July 2015, median age = 69.0, males = 78, adenocarcinomas = 79). Texture metrics were obtained using a machine-learning algorithm for automatic PET segmentation. A Cox regression model including age, radiological stage, treatment and 16 texture metrics was developed. Patients were stratified into quartiles according to a prognostic score derived from the model. A p-value < 0.05 was considered statistically significant. Primary outcome was overall survival (OS). RESULTS: Six variables were significantly and independently associated with OS: age [HR =1.02 (95% CI 1.01-1.04), p < 0.001], radiological stage [1.49 (1.20-1.84), p < 0.001], treatment [0.34 (0.24-0.47), p < 0.001], log(TLG) [5.74 (1.44-22.83), p = 0.013], log(Histogram Energy) [0.27 (0.10-0.74), p = 0.011] and Histogram Kurtosis [1.22 (1.04-1.44), p = 0.017]. The prognostic score demonstrated significant differences in OS between quartiles in both the development (X2 143.14, df 3, p < 0.001) and validation cohorts (X2 20.621, df 3, p < 0.001). CONCLUSIONS: This prognostic model can risk stratify patients and demonstrates the additional benefit of PET texture analysis in OC staging. KEY POINTS: • PET texture analysis adds prognostic value to oesophageal cancer staging. • Texture metrics are independently and significantly associated with overall survival. • A prognostic model including texture analysis can help risk stratify patients.

Successful treatment of residual pituitary adenoma in persistent acromegaly following localisation by 11C-methionine PET co-registered with MRI.

OBJECTIVE: To determine if functional imaging using 11C-methionine positron emission tomography co-registered with 3D gradient echo MRI (Met-PET/MRI), can identify sites of residual active tumour in treated acromegaly, and discriminate these from post-treatment change, to allow further targeted treatment. DESIGN/METHODS: Twenty-six patients with persistent acromegaly after previous treatment, in whom MRI appearances were considered indeterminate, were referred to our centre for further evaluation over a 4.5-year period. Met-PET/MRI was performed in each case, and findings were used to decide regarding adjunctive therapy. Four patients with clinical and biochemical remission after transsphenoidal surgery (TSS), but in whom residual tumour was suspected on post-operative MRI, were also studied. RESULTS: Met-PET/MRI demonstrated tracer uptake only within the normal gland in the four patients who had achieved complete remission after primary surgery. In contrast, in 26 patients with active acromegaly, Met-PET/MRI localised sites of abnormal tracer uptake in all but one case. Based on these findings, fourteen subjects underwent endoscopic TSS, leading to a marked improvement in (n = 7), or complete resolution of (n = 7), residual acromegaly. One patient received stereotactic radiosurgery and two patients with cavernous sinus invasion were treated with image-guided fractionated radiotherapy, with good disease control. Three subjects await further intervention. Five patients chose to receive adjunctive medical therapy. Only one patient developed additional pituitary deficits after Met-PET/MRI-guided TSS. CONCLUSIONS: In patients with persistent acromegaly after primary therapy, Met-PET/MRI can help identify the site(s) of residual pituitary adenoma when MRI appearances are inconclusive and direct further targeted intervention (surgery or radiotherapy).

Gender, body weight, disease activity, and previous radiotherapy influence the response to pegvisomant.

CONTEXT/OBJECTIVE: To effectively normalize IGF-I in patients with acromegaly, various covariates may affect dosing and plasma concentrations of pegvisomant. We assessed whether sex, age, weight, and previous radiotherapy influence dosing of pegvisomant in patients with active disease. DESIGN: Data from 69 men and 49 women participating in multicenter, open-label trials of pegvisomant were retrospectively evaluated using multiple regression techniques. Sixty-nine subjects (39 men, 30 women) had undergone external beam pituitary radiotherapy. Serum IGF-I was at least 30% above age-related upper limit of normal in all patients at study entry. After a loading dose of pegvisomant (80 mg), patients were commenced on 10 mg/d. Pegvisomant dose was adjusted by 5 mg every eighth week until serum IGF-I was normalized. RESULTS: At baseline, men had significantly higher mean serum IGF-I levels than women despite similar GH levels. After treatment with pegvisomant, IGF-I levels were similar in men and women. A significant correlation between baseline GH, IGF-I, body weight, and the dose of pegvisomant required to normalize serum IGF-I was observed (all P < 0.001). Women required an average of 0.04 mg/kg more pegvisomant than men and a mean weight-corrected dose of 19.2 mg/d to normalize serum IGF-I [14.5 mg/d (men); P < 0.001]. Patients treated with radiotherapy required less pegvisomant to normalize serum IGF-I despite similar baseline GH/IGF-I levels (15.2 vs. 18.5 mg/d for no previous radiotherapy; P = 0.002). CONCLUSIONS: Sex, body weight, previous radiotherapy, and baseline GH/IGF-I influence the dose of pegvisomant required to normalize serum IGF-I in patients with active acromegaly.

High levels of 150-kDa insulin-like growth factor binding protein three ternary complex in patients with acromegaly and the effect of pegvisomant-induced serum IGF-I normalization.

OBJECTIVE: To assess the effect of pegvisomant-induced serum insulin-like growth factor 1 (IGF-1) normalization on IGF binding proteins 1, 2, 3 (IGFBP-1, IGFBP-2 and IGFBP-3), total, non-bound (45 kDa) and 150-kDa ternary complex-associated IGFBP-3, and in vivo IGFBP-3 proteolysis in patients with active acromegaly. DESIGN: The above parameters were measured in 16 patients (median age 57 (range 27-78)) with active acromegaly (serum IGF-I at least 30% above the upper limit of an age-related reference range after washout) in a paired manner on samples obtained after washout and the first occurrence of serum IGF-I normalization during pegvisomant therapy (median dose 15 mg/day (10-40 mg)). RESULTS: Total IGFBP-3 and 150-kDa ternary complex-associated IGFBP-3 were significantly elevated in patients at baseline compared to controls ((mean+/-SEM) 4345+/-194 vs. 3456+/-159 microg/L, P<0.01 and 3908+/-160 va. 3042+/-149 microg/L, P<0.01, respectively), but no significant difference in 45-kDa IGFBP-3 or in vivo IGFBP-3 proteolysis was observed. Serum IGF-I normalization (699+/-76 to 242+/-28 microg/L, P<0.0001) was associated with a fall in total IGFBP-3 (4345+/-194 to 3283+/-160 microg/L, P<0.001) due to a reduction in 150-kDa ternary complex-associated IGFBP-3 (3908+/-160 to 3008+/-140 microg/L, P<0.0001). 45 kDa IGFBP-3 and in vivo IGFBP-3 proteolysis were unaffected by GH receptor blockade (326+/-13 to 330+/-18 microg/L, P=0.86; 30+/-3.5 to 30+/-3.9%, P=0.75, respectively). CONCLUSIONS: GH receptor blockade in patients with acromegaly lowers IGF-I and 150-kDa IGFBP-3 ternary complex formation. 50 kDa ternary complex formation (not in vivo IGFBP-3 proteolysis) is GH dependent and measurement of 150-kDa ternary complex-associated IGFBP-3 may provide useful information regarding treatment efficacy in patients with acromegaly.