| 1. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 2. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
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| 4. |
- Alabi, RO, et al.
(författare)
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Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review
- 2021
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Ingår i: Frontiers in oral health. - : Frontiers Media SA. - 2673-4842. ; 2, s. 686863-
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases—PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.
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| 6. |
- Almangush, A, et al.
(författare)
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Improving Risk Stratification of Early Oral Tongue Cancer with TNM-Immune (TNM-I) Staging System
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:13
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Tidskriftsartikel (refereegranskat)abstract
- Although patients with early-stage oral tongue squamous cell carcinoma (OTSCC) show better survival than those with advanced disease, there is still a number of early-stage cases who will suffer from recurrence, cancer-related mortality and worse overall survival. Incorporation of an immune descriptive factor in the staging system can aid in improving risk assessment of early OTSCC. A total of 290 cases of early-stage OTSCC re-classified according to the American Joint Committee on Cancer (AJCC 8) staging were included in this study. Scores of tumor-infiltrating lymphocytes (TILs) were divided as low or high and incorporated in TNM AJCC 8 to form our proposed TNM-Immune system. Using AJCC 8, there were no significant differences in survival between T1 and T2 tumors (p > 0.05). Our proposed TNM-Immune staging system allowed for significant discrimination in risk between tumors of T1N0M0-Immune vs. T2N0M0-Immune. The latter associated with a worse overall survival with hazard ratio (HR) of 2.87 (95% CI 1.92–4.28; p < 0.001); HR of 2.41 (95% CI 1.26–4.60; p = 0.008) for disease-specific survival; and HR of 1.97 (95% CI 1.13–3.43; p = 0.017) for disease-free survival. The TNM-Immune staging system showed a powerful ability to identify cases with worse survival. The immune response is an important player which can be assessed by evaluating TILs, and it can be implemented in the staging criteria of early OTSCC. TNM-Immune staging forms a step towards a more personalized classification of early OTSCC.
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| 7. |
- Almangush, A, et al.
(författare)
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Stromal categorization in early oral tongue cancer
- 2021
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Ingår i: Virchows Archiv : an international journal of pathology. - : Springer Science and Business Media LLC. - 1432-2307. ; 478:5, s. 925-932
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Tidskriftsartikel (refereegranskat)abstract
- Stromal categorization has been used to classify many epithelial cancer types. We assessed the desmoplastic reaction and compared its significance with other stromal characteristics in early (cT1-2N0) oral tongue squamous cell carcinoma (OTSCC). In this multi-institutional study, we included 308 cases treated for early OTSCC at five Finnish university hospitals or at the A.C. Camargo Cancer Center in São Paulo, Brazil. The desmoplastic reaction was classified as immature, intermediate, or mature based on the amount of hyalinized keloid-like collagen and myxoid stroma. We compared the prognostic value of the desmoplastic reaction with a stromal grading system based on tumor-stroma ratio and stromal tumor-infiltrating lymphocytes. We found that a high amount of stroma with a weak infiltration of lymphocytes was associated statistically significantly with a worse disease-free survival with a hazard ratio (HR) of 2.68 (95% CI 1.26–5.69), worse overall survival (HR 2.95, 95% CI 1.69–5.15), and poor disease-specific survival (HR 2.66, 95% CI 1.11–6.33). Tumors having a high amount of stroma with a weak infiltration of lymphocytes were also significantly associated with a high rate of local recurrence (HR 4.13, 95% CI 1.67–10.24), but no significant association was found with lymph node metastasis (HR 1.27, 95% CI 0.37–4.35). Categorization of the stroma based on desmoplastic reaction (immature, intermediate, mature) showed a low prognostic value for early OTSCC in all survival analyses (P > 0.05). In conclusion, categorization of the stroma based on the amount of stroma and its infiltrating lymphocytes shows clinical relevance in early OTSCC superior to categorization based on the maturity of stroma.
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