Fiona M. Fennessy

Bio: Fiona M. Fennessy is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Prostate cancer & Uterine fibroids. The author has an hindex of 36, co-authored 114 publications receiving 7897 citations. Previous affiliations of Fiona M. Fennessy include Royal College of Surgeons in Ireland & Thomas Jefferson University.

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution

Abstract: The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies.

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.

Abstract: Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n = 353) and verified them in an independent validation cohort (n = 352) All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter) We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR+ and EGFR- cases (AUC = 069) Combining this signature with a clinical model of EGFR status (AUC = 070) significantly improved prediction accuracy (AUC = 075) The highest performing signature was capable of distinguishing between EGFR+ and KRAS+ tumors (AUC = 080) and, when combined with a clinical model (AUC = 081), substantially improved its performance (AUC = 086) A KRAS+/KRAS- radiomic signature also showed significant albeit lower performance (AUC = 063) and did not improve the accuracy of a clinical predictor of KRAS status Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost Cancer Res; 77(14); 3922-30 ©2017 AACR

Uterine leiomyomas : MR imaging-guided focused ultrasound surgery-results of different treatment protocols

Abstract: Purpose: To prospectively assess patient response (after 12 months) to magnetic resonance (MR) imaging–guided focused ultrasound surgery in treatment of uterine leiomyomas by using two treatment protocols. Materials and Methods: This prospective clinical trial was approved by institutional review boards and was HIPAA compliant. After giving informed consent, patients with symptomatic leiomyomas were consecutively enrolled and treated at one of five U.S. centers by using an original or a modified protocol. Outcomes were assessed with the symptom severity score (SSS) obtained at baseline and 3, 6, and 12 months after treatment. Adverse events (AEs) were recorded. Statistical analysis included Student t test, Fisher exact test, analysis of covariance, Spearman correlation, and logistic regression. Results: One hundred sixty patients had a mean SSS of 62.1 ± 16.3 (standard deviation) at baseline, which decreased to 35.5 ± 19.5 at 3 months (P < .001) and to 32.3 ± 19.8 at 6 months (P < .001) and was 32.7 ± 21....

Uterine leiomyomas: MR imaging-based thermometry and thermal dosimetry during focused ultrasound thermal ablation.

Abstract: Purpose: To retrospectively evaluate magnetic resonance (MR) imaging–based thermometry and thermal dosimetry during focused ultrasound treatments of uterine leiomyomas (ie, fibroids). Materials and Methods: All patients gave written informed consent for the focused ultrasound treatments and the current HIPAA-compliant retrospective study, both of which were institutional review board approved. Thermometry performed during the treatments of 64 fibroids in 50 women (mean age, 46.6 years ± 4.5 [standard deviation]) was used to create thermal dose maps. The areas that reached dose values of 240 and 18 equivalent minutes at 43°C were compared with the nonperfused regions measured on contrast material–enhanced MR images by using the Bland-Altman method. Volume changes in treated fibroids after 6 months were compared with volume changes in nontreated fibroids and with MR-based thermal dose estimates. Results: While the thermal dose estimates were shown to have a clear relationship with resulting nonperfused regi...

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

Abstract: This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Abstract: Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

Computational Radiomics System to Decode the Radiographic Phenotype

Abstract: Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.