One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types.
A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90%±8%, sensitivity of 89%±9%, and specificity of 91%±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94%±6%, sensitivity of 94%±6%, and specificity of 95%±6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.
Background: We compared overall survival (OS) between radiation therapy (RT) and chemoradiotherapy (CRT) in patients with anaplastic thyroid carcinoma (ATC) using a large database. Methods: The National Cancer Data Base was queried for ATC patients diagnosed between 2004 and 2013 who received RT or CRT. Groups were balanced by propensity score matching (PSM) on nine relevant variables. OS was also examined in five paired subgroups given known patient heterogeneity. Results: Of 858 total patients, 575 received CRT and 283 received RT. CRT was associated with decreased risk of death (hazard ratio [HR] 0.66, P <.001), 1-year OS 25.5% vs 14.3%. A survival advantage to CRT was seen using PSM cohorts (HR 0.75, P =.006). Those receiving definitive surgery saw the greatest benefit to CRT over RT (HR 0.65, P =.009), 1-year OS 39.6% vs 20.4%. Conclusions: CRT is associated with decreased risk of death in ATC; the magnitude of CRT vs RT benefit varied by subgroup.
Purpose: Previous studies revealed diverging results regarding the role of survivin in squamous cell carcinoma of the head and neck (SCCHN). This study aimed to evaluate the clinical significance of survivin expression in SCCHN; the function of survivin in DNA-damage repair following ionizing radiation therapy (RT) in SCCHN cells; and the potential of honokiol to enhance RT through downregulation of survivin. Experimental Design: Expression of survivin in SCCHN patient primary tumor tissues (n ¼ 100) was analyzed and correlated with clinical parameters. SCCHN cell lines were used to evaluate the function of survivin and the effects of honokiol on survivin expression in vitro and in vivo. Results: Overexpression of survivin was significantly associated with lymph nodes' metastatic status (P ¼ 0.025), worse overall survival (OS), and disease-free survival (DFS) in patients receiving RT (n ¼ 65, OS: P ¼ 0.024, DFS: P ¼ 0.006) and in all patients with SCCHN (n ¼ 100, OS: P ¼ 0.002, DFS: P ¼ 0.003). In SCCHN cells, depletion of survivin led to increased DNA damage and cell death following RT, whereas overexpression of survivin increased clonogenic survival. RT induced nuclear accumulation of survivin and its molecular interaction with g-H2AX and DNA-PKCs. Survivin specifically bound to DNA DSB sites induced by I-SceI endonuclease. Honokiol (which downregulates survivin expression) in combination with RT significantly augmented cytotoxicity in SCCHN cells with acquired radioresistance and inhibited growth in SCCHN xenograft tumors. Conclusions: Survivin is a negative prognostic factor and is involved in DNA-damage repair induced by RT. Targeting survivin using honokiol in combination with RT May provide novel therapeutic opportunities.
Superselective neck dissection, defined as dissection of two or less contiguous neck levels, has recently been introduced to reduce surgical morbidity of neck dissection while maintaining favorable oncologic outcomes. The purpose of this review is to report the results of superselective neck dissection when applied to specific settings: the management of regional disease after chemoradiation, head and neck squamous cell carcinoma with clinical N0 necks, and high risk papillary thyroid carcinoma.
Purpose: This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers. Experimental Design: A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathologic diagnosis. More than two million reflectance spectral signatures were obtained by HSI and analyzed using machine-learning methods. The detection results of HSI were compared with autofluorescence imaging and fluorescence imaging of two vital-dyes of the same specimens. Results: Quantitative HSI differentiated cancerous tissue from normal tissue in ex vivo surgical specimens with a sensitivity and specificity of 91% and 91%, respectively, and which was more accurate than autofluorescence imaging (P < 0.05) or fluorescence imaging of 2-NBDG (P < 0.05) and proflavine (P < 0.05). The proposed quantification tools also generated cancer probability maps with the tumor border demarcated and which could provide real-time guidance for surgeons regarding optimal tumor resection. Conclusions: This study highlights the feasibility of using quantitative HSI as a diagnostic tool to delineate the cancer boundaries in surgical specimens, and which could be translated into the clinic application with the hope of improving clinical outcomes in the future.
Surgical resection of head and neck (H and N) squamous cell carcinoma (SCC) may yield inadequate surgical cancer margins in 10 to 20% of cases. This study investigates the performance of label-free, reflectance-based hyperspectral imaging (HSI) and autofluorescence imaging for SCC detection at the cancer margin in excised tissue specimens from 102 patients and uses fluorescent dyes for comparison. Fresh surgical specimens (n = 293) were collected during H and N SCC resections (n = 102). The tissue specimens were imaged with reflectance-based HSI and autofluorescence imaging and afterwards with two fluorescent dyes for comparison. A histopathological ground truth was made. Deep learning tools were developed to detect SCC with new patient samples (inter-patient) and machine learning for intra-patient tissue samples. Area under the curve (AUC) of the receiver-operator characteristic was used as the main evaluation metric. Additionally, the performance was estimated in mm increments circumferentially from the tumor-normal margin. In intra-patient experiments, HSI classified conventional SCC with an AUC of 0.82 up to 3 mm from the cancer margin, which was more accurate than proflavin dye and autofluorescence (both p < 0.05). Intra-patient autofluorescence imaging detected human papilloma virus positive (HPV+) SCC with an AUC of 0.99 at 3 mm and greater accuracy than proflavin dye (p < 0.05). The inter-patient results showed that reflectance-based HSI and autofluorescence imaging outperformed proflavin dye and standard red, green, and blue (RGB) images (p < 0.05). In new patients, HSI detected conventional SCC in the larynx, oropharynx, and nasal cavity with 0.85–0.95 AUC score, and autofluorescence imaging detected HPV+ SCC in tonsillar tissue with 0.91 AUC score. This study demonstrates that label-free, reflectance-based HSI and autofluorescence imaging methods can accurately detect the cancer margin in ex-vivo specimens within minutes. This non-ionizing optical imaging modality could aid surgeons and reduce inadequate surgical margins during SCC resections.
Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.
BACKGROUND: There is conflicting evidence regarding the role of peritumoral lymphatic vessel density (LVD) and blood microvessel density (MVD) in the metastasis and prognosis of head and neck squamous cell carcinoma (HNSCC). Existing studies are limited to one or two head and neck subsites and/or small sample sizes. A larger study incorporating multiple sub-sites is needed to address the role of peritumoral LVD and MVD in HNSCC metastasis and prognosis. METHODS: Tissue samples from 200 HNSCC cases were stained simultaneously using immunohistochemistry (IHC) for markers of peritumoral LVD (lymphatic vessel marker D240) and MVD (blood vessel marker CD31). Of the stained slides, 166 and 167 were evaluable for LVD and MVD, respectively. The results were then correlated with clinicopathologic features and patient outcomes. RESULTS: Patients with metastatic disease were more likely to have high peritumoral MVD. Through multivariable analyses, MVD was not significantly related to DFS and OS, while low LVD was related to higher risk of disease progression and poor survival. CONCLUSIONS: Peritumoral MVD was found to be positively associated with metastasis, while LVD was found to be inversely related to both metastasis and progression of HNSCC. These findings may suggest a prognostic role of both peritumoral LVD and MVD in patients with HNSCC.