Comparison of deep-learning data fusion strategies in mandibular osteoradionecrosis prediction modelling using clinical variables and radiation dose distribution volumes

Purpose. NTCP modelling is rapidly embracing DL methods as the need to include spatial dose information is acknowledged. Finding the most appropriate way of combining radiation dose distribution images and clinical data involves technical challenges and requires domain knowledge. We propose different data fusion strategies that we hope will serve as a starting point for future DL NTCP studies. Methods. Early, joint and late DL multi-modality fusion strategies were compared using clinical variables and mandibular radiation dose distribution volumes. The discriminative performance of the multi-modality models was compared to that of single-modality models. All the experiments were conducted on a control-case matched cohort of 92 ORN cases and 92 controls from a single institution. Results. The highest ROC AUC score was obtained with the late fusion model (0.70), but no statistically significant differences in discrimination performance were observed between strategies. While late fusion was the least technically complex strategy, its design did not model the inter-modality interactions that are required for NTCP modelling. Joint fusion involved the most complex design but resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation. Conclusions. This is the first study that compares different strategies for including image data into DL NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. We encourage future DL NTCP studies to report on different fusion strategies to better justify their choice of DL pipeline.Comment: 10 pages, 4 figures, 3 table

Pre-operative tracheostomy does not impact on stomal recurrence and overall survival in patients undergoing primary laryngectomy

Pre-operative tracheostomy (POT) to secure a critical airway up to several weeks before definitive laryngectomy in patients with laryngeal cancer has been proposed as a risk factor for poor oncologic outcome. Few modern papers, however, examine this question. The aim of this study is therefore to determine whether POT affects oncologic outcome with an emphasis on stomal/peristomal recurrence. This is a retrospective case note review of 60 consecutive patients undergoing curative primary total laryngectomy (TL) for advanced laryngeal squamous cell carcinoma (SCC). Demographic, staging, treatment and outcome data were collected. 27/60 (45%) patients had POT and 33/60 did not. No patient underwent laser debulking. Median age was 62years (39-90years) and median follow-up of survivors was 31months. 5-year overall survival (OS), disease-specific survival (DSS) and local recurrence-free survival (LRFS) of patients undergoing POT versus no POT was 28 versus 39% (p=0.947), 55 versus 46% (p=0.201) and 96 versus 88% (p=0.324) respectively. No statistically significant difference in OS, DSS and LRFS was found between patients undergoing POT and those not. Despite the relatively small case series, this evidence should reassure surgeons without the ability to perform trans-oral debulking that they should not hesitate to perform tracheostomy on a patient with airway obstruction due to laryngeal cancer. Appropriate definitive treatment meant that POT was not a risk factor for poor oncological outcome in our serie

An assessment of the use of patient reported outcome measurements (PROMs) in cancers of the pelvic abdominal cavity: identifying oncologic benefit and an evidence-practice gap in routine clinical practice.

BACKGROUND: Patient reported outcome measurements (PROMs) are emerging as an important component of patient management in the cancer setting, providing broad perspectives on patients' quality of life and experience. The use of PROMs is, however, generally limited to the context of randomised control trials, as healthcare services are challenged to sustain high quality of care whilst facing increasing demand and financial shortfalls. We performed a systematic review of the literature to identify any oncological benefit of using PROMs and investigate the wider impact on patient experience, in cancers of the pelvic abdominal cavity specifically. METHODS: A systematic review of the literature was conducted using MEDLINE (Pubmed) and Ovid Gateway (Embase and Ovid) until April 2020. Studies investigating the oncological outcomes of PROMs were deemed suitable for inclusion. RESULTS: A total of 21 studies were included from 2167 screened articles. Various domains of quality of life (QoL) were identified as potential prognosticators for oncologic outcomes in cancers of the pelvic abdominal cavity, independent of other clinicopathological features of disease: 3 studies identified global QoL as a prognostic factor, 6 studies identified physical and role functioning, and 2 studies highlighted fatigue. In addition to improved outcomes, a number of included studies also reported that the use of PROMs enhanced both patient-clinician communication and patient satisfaction with care in the clinical setting. CONCLUSIONS: This review highlights the necessity of routine collection of PROMs within the pelvic abdominal cancer setting to improve patient quality of life and outcomes

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

Association between hypoxic volume and underlying hypoxia-induced gene expression in oropharyngeal squamous cell carcinoma (OPSCC):Hypoxia biomarkers from 64Cu-ATSM PET/CT imaging

Background: Hypoxia imaging is a promising tool for targeted therapy but the links between imaging features and underlying molecular characteristics of the tumour have not been investigated. The aim of this study was to compare hypoxia biomarkers and gene expression in oropharyngeal squamous cell carcinoma (OPSCC) diagnostic biopsies with hypoxia imaged with 64Cu-ATSM PET/CT. Methods: 64Cu-ATSM imaging, molecular and clinical data were obtained for 15 patients. Primary tumour SUVmax, tumour to muscle ratio (TMR) and hypoxic volume were tested for association with reported hypoxia gene signatures in diagnostic biopsies. A putative gene signature for hypoxia in OPSCCs (hypoxic volume-associated gene signature (HVS)) was derived. Results: Hypoxic volume was significantly associated with a reported hypoxia gene signature (rho=0.57, P=0.045), but SUVmax and TMR were not. Immunohistochemical staining with the hypoxia marker carbonic anhydrase 9 (CA9) was associated with a gene expression hypoxia response (rho=0.63, P=0.01). Sixteen genes were positively and five genes negatively associated with hypoxic volume (adjusted P<0.1; eight genes had adjusted P<0.05; HVS). This signature was associated with inferior 3-year progression-free survival (HR=1.5 (1.0–2.2), P=0.047) in an independent patient cohort. Conclusions: 64Cu-ATSM-defined hypoxic volume was associated with underlying hypoxia gene expression response. A 21-gene signature derived from hypoxic volume from patients with OPSCCs in our study may be linked to progression-free survival

A method for accurate spatial registration of PET images and histopathology slices

Background: Accurate alignment between histopathology slices and positron emission tomography (PET) images is important for radiopharmaceutical validation studies. Limited data is available on the registration accuracy that can be achieved between PET and histopathology slices acquired under routine pathology conditions where slices may be non-parallel, non-contiguously cut and of standard block size. The purpose of this study was to demonstrate a method for aligning PET images and histopathology slices acquired from patients with laryngeal cancer and to assess the registration accuracy obtained under these conditions. Methods: Six subjects with laryngeal cancer underwent a 64Cu-copper-II-diacetyl-bis(N4-methylthiosemicarbazone) (64Cu-ATSM) PET computed tomography (CT) scan prior to total laryngectomy. Sea urchin spines were inserted into the pathology specimen to act as fiducial markers. The specimen was fixed in formalin, as per standard histopathology operating procedures, and was then CT scanned and cut into millimetre-thick tissue slices. A subset of the tissue slices that included both tumour and fiducial markers was taken and embedded in paraffin blocks. Subsequently, microtome sectioning and haematoxylin and eosin staining were performed to produce 5-μm-thick tissue sections for microscopic digitisation. A series of rigid registration procedures was performed between the different imaging modalities (PET; in vivo CT—i.e. the CT component of the PET-CT; ex vivo CT; histology slices) with the ex vivo CT serving as the reference image. In vivo and ex vivo CTs were registered using landmark-based registration. Histopathology and ex vivo CT images were aligned using the sea urchin spines with additional anatomical landmarks where available. Registration errors were estimated using a leave-one-out strategy for in vivo to ex vivo CT and were estimated from the RMS landmark accuracy for histopathology to ex vivo CT. Results: The mean ± SD accuracy for registration of the in vivo to ex vivo CT images was 2.66 ± 0.66 mm, and the accuracy for registration of histopathology to ex vivo CT was 0.86 ± 0.41 mm. Estimating the PET to in vivo CT registration accuracy to equal the PET-CT alignment accuracy of 1 mm resulted in an overall average registration error between PET and histopathology slices of 3.0 ± 0.7 mm. Conclusions: We have developed a registration method to align PET images and histopathology slices with an accuracy comparable to the spatial resolution of the PET images.</p