Immunotherapy is a novel anti-cancer treatment that shows significant improvements in outcomes for lung cancer patients. However, this treatment has the potential for substantial side effects in a minority of patients, and many lung cancer patients do not benefit from it. Programmed-death ligand-1 expression in tumour cells is currently the main biomarker used to identify those who might benefit but it is not very accurate. Tumour mutational burden (TMB) is a promising alternative, with lung cancers having more than 10 mutations/megabase being more likely to respond to immunotherapy. However, the cost and time it takes to obtain TMB makes it difficult to implement in the clinic. In this study, we used the deep learning technique of transfer learning with Alexnet to obtain a model that can estimate whether a cancer is highly mutated or not based on digitized hematoxylin and eosin histology slides that are routinely obtained from surgical resection of squamous cell carcinoma. The system was developed using images from 20 patients obtained through The Cancer Genome Atlas, five of which were reserved for validation. On this validation set, the system had an area under the receiver operator characteristic curve of 0.80, error rate of 24%, false negative rate of 26%, and false positive rate of 22%. This motivates additional work in this direction to build a system that can be used in the future to inform physicians as to which patients with squamous lung carcinoma would benefit from immunotherapy.
Sean A. Pentinga, Keith Kwan, Sarah Mattonen, Carol Johnson, Alexander Louie, Mark Landis, Richard Inculet, Richard Malthaner, Dalilah Fortin, George Rodrigues, Brian Yaremko, David Palma, Aaron Ward
Stereotactic ablative radiotherapy (SABR) delivers high-dose-per-fraction radiotherapy to tumours and spares surrounding tissue. It is effective for early-stage non-small cell lung cancer. However, SABR causes radiationinduced lung injuries that mimic recurring cancer, confounding detection of recurrences and early salvage therapy. We have previously developed radiomics-based recurrence detection. However, our radiomics system needs to be validated against histologic markers of viable tumour post-SABR. In this paper, our goals were to develop semiautomatic (1) 2D reconstruction of pseudo whole-mount (PWM) tissue sections from scanned slides, (2) 3D reconstruction and registration of PWM sections to pre-surgery computed tomography (CT), and (3) quantitative registration error measurement. Lobectomy tissue sections on standard 1” × 3” slides were obtained from patients who underwent SABR. Our graphical user interface allows interactive stitching of the sections into PWMs. Using our developed 3D Slicer-based thin-plate spline warping tool, we performed 3D PWM reconstruction and registered them to CT via correspondence of homologous intrinsic landmarks. The target registration error for 229 fiducial pairs defining vessels and airways was calculated for 56 PWMs reconstructed from 9 patients. We measured a mean of 7.33 mm, standard deviation of 4.59 mm and root mean square of 8.65 mm. This proof-of-principle study demonstrates for the first time that it is feasible to register in vivo human lung CT images with histology, with no modifications to the clinical pathology workflow other than videography to document gross dissection. Ongoing work to automate this process will yield a tool for histologic lung imaging and radiomics validation.
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