scholarly journals Parosteal Osteosarcoma: Radiologic and Prognostic Features

2021 ◽  
Vol 54 (2) ◽  
pp. 239-246
Author(s):  
Osman Emre Aycan ◽  
Muhammet Alptekin Kocaoğlu ◽  
Muhammet Coşkun Arslan ◽  
Muhammed Mert ◽  
Alper Köksal
Keyword(s):  
Parosteal Osteosarcoma ◽  
Prognostic Features

Melanoma: Prognostic Factors and Factors Predictive of Response to Therapy

2020 ◽  
Vol 27 (17) ◽  
pp. 2792-2813
Author(s):  
Martina Strudel ◽  
Lucia Festino ◽  
Vito Vanella ◽  
Massimiliano Beretta ◽  
Francesco M. Marincola ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Lactate Dehydrogenase ◽  
Checkpoint Inhibitors ◽  
Survival Rates ◽  
Elevated Serum ◽  
Predictive Biomarkers ◽  
Response To Therapy ◽  
Response To Treatment ◽  
Prognostic Features ◽  
Programmed Death

Background: A better understanding of prognostic factors and biomarkers that predict response to treatment is required in order to further improve survival rates in patients with melanoma. Predictive Biomarkers: The most important histopathological factors prognostic of worse outcomes in melanoma are sentinel lymph node involvement, increased tumor thickness, ulceration and higher mitotic rate. Poorer survival may also be related to several clinical factors, including male gender, older age, axial location of the melanoma, elevated serum levels of lactate dehydrogenase and S100B. Predictive Biomarkers: Several biomarkers have been investigated as being predictive of response to melanoma therapies. For anti-Programmed Death-1(PD-1)/Programmed Death-Ligand 1 (PD-L1) checkpoint inhibitors, PD-L1 tumor expression was initially proposed to have a predictive role in response to anti-PD-1/PD-L1 treatment. However, patients without PD-L1 expression also have a survival benefit with anti-PD-1/PD-L1 therapy, meaning it cannot be used alone to select patients for treatment, in order to affirm that it could be considered a correlative, but not a predictive marker. A range of other factors have shown an association with treatment outcomes and offer potential as predictive biomarkers for immunotherapy, including immune infiltration, chemokine signatures, and tumor mutational load. However, none of these have been clinically validated as a factor for patient selection. For combined targeted therapy (BRAF and MEK inhibition), lactate dehydrogenase level and tumor burden seem to have a role in patient outcomes. Conclusions: With increasing knowledge, the understanding of melanoma stage-specific prognostic features should further improve. Moreover, ongoing trials should provide increasing evidence on the best use of biomarkers to help select the most appropriate patients for tailored treatment with immunotherapies and targeted therapies.

scholarly journals Parosteal Osteosarcoma with Pancreatic Metastasis and Multiple Relapses: A Case Report and Review of the Literature

2021 ◽  
pp. 983-988
Author(s):  
Daniel Cirotski ◽  
Jyoti Panicker
Keyword(s):  
Age Groups ◽  
Bone Cancer ◽  
Pancreatic Metastasis ◽  
Low Grade ◽  
Parosteal Osteosarcoma ◽  
Disease Course ◽  
Review Of The Literature ◽  
Histologic Subtype ◽  
Primary Bone ◽  
Multiple Relapses

Osteosarcoma is the most common primary bone cancer in all age groups. Metastasis mostly occurs with high-grade tumors disseminating to the lungs and other bones. Spread to the pancreas is rare and undocumented in the low-grade subtypes. Additionally, it is uncommon for the disease course of low-grade subtypes to involve multiple relapses. We present a 35-year-old woman with parosteal osteosarcoma who has experienced an atypical metastasis to the pancreas as well as multiple local and pulmonary relapses. The lesion was identified incidentally on routine imaging, and the patient underwent resection. We compare our case to the other reports of pancreatic metastasis in the literature. Despite being especially rare, clinicians ought to be aware of pancreatic metastasis of osteosarcoma. Furthermore, despite parosteal osteosarcoma’s less aggressive disease course, it can uncommonly lead to multiple relapses. We present a rare case exemplifying these phenomena in the prognostically favorable histologic subtype of parosteal osteosarcoma.

scholarly journals Monocarboxylate transporter-1 (MCT1) protein expression in head and neck cancer affects clinical outcome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Leu ◽  
J. Kitz ◽  
Y. Pilavakis ◽  
S. Hakroush ◽  
H. A. Wolff ◽  
...  
Keyword(s):  
Cell Membrane ◽  
Protein Expression ◽  
Head And Neck ◽  
Cox Regression ◽  
Locally Advanced ◽  
Monocarboxylate Transporter ◽  
Positive Staining ◽  
Monocarboxylate Transporter 1 ◽  
Prognostic Features ◽  
Hpv Status

AbstractTreatment of locally advanced, unresectable head and neck squamous cell carcinoma (HNSCC) often yields only modest results with radiochemotherapy (RCT) as standard of care. Prognostic features related to outcome upon RCT might be highly valuable to improve treatment. Monocarboxylate transporters-1 and -4 (MCT1/MCT4) were evaluated as potential biomarkers. A cohort of HNSCC patients without signs for distant metastases was assessed eliciting 82 individuals eligible whereof 90% were diagnosed with locally advanced stage IV. Tumor specimens were stained for MCT1 and MCT4 in the cell membrane by immunohistochemistry. Obtained data were evaluated with respect to overall (OS) and progression-free survival (PFS). Protein expression of MCT1 and MCT4 in cell membrane was detected in 16% and 85% of the tumors, respectively. Expression of both transporters was not statistically different according to the human papilloma virus (HPV) status. Positive staining for MCT1 (n = 13, negative in n = 69) strongly worsened PFS with a hazard ratio (HR) of 3.1 (95%-confidence interval 1.6–5.7, p < 0.001). OS was likewise affected with a HR of 3.8 (2.0–7.3, p < 0.001). Multivariable Cox regression confirmed these findings. We propose MCT1 as a promising biomarker in HNSCC treated by primary RCT.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

scholarly journals Identifying prognostic factors of severe metabolic acidosis and uraemia in African children with severe falciparum malaria: a secondary analysis of a randomized trial

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Grace Mzumara ◽  
Stije Leopold ◽  
Kevin Marsh ◽  
Arjen Dondorp ◽  
Eric O. Ohuma ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Metabolic Acidosis ◽  
Falciparum Malaria ◽  
Model Building ◽  
Statistical Significance ◽  
Severe Metabolic Acidosis ◽  
African Countries ◽  
Prognostic Features ◽  
African Children ◽  
Severe Falciparum Malaria

Abstract Background Severe metabolic acidosis and acute kidney injury are major causes of mortality in children with severe malaria but are often underdiagnosed in low resource settings. Methods A retrospective analysis of the ‘Artesunate versus quinine in the treatment of severe falciparum malaria in African children’ (AQUAMAT) trial was conducted to identify clinical features of severe metabolic acidosis and uraemia in 5425 children from nine African countries. Separate models were fitted for uraemia and severe metabolic acidosis. Separate univariable and multivariable logistic regression were performed to identify prognostic factors for severe metabolic acidosis and uraemia. Both analyses adjusted for the trial arm. A forward selection approach was used for model building of the logistic models and a threshold of 5% statistical significance was used for inclusion of variables into the final logistic model. Model performance was assessed through calibration, discrimination, and internal validation with bootstrapping. Results There were 2296 children identified with severe metabolic acidosis and 1110 with uraemia. Prognostic features of severe metabolic acidosis among them were deep breathing (OR: 3.94, CI 2.51–6.2), hypoglycaemia (OR: 5.16, CI 2.74–9.75), coma (OR: 1.72 CI 1.17–2.51), respiratory distress (OR: 1.46, CI 1.02–2.1) and prostration (OR: 1.88 CI 1.35–2.59). Features associated with uraemia were coma (3.18, CI 2.36–4.27), Prostration (OR: 1.78 CI 1.37–2.30), decompensated shock (OR: 1.89, CI 1.31–2.74), black water fever (CI 1.58. CI 1.09–2.27), jaundice (OR: 3.46 CI 2.21–5.43), severe anaemia (OR: 1.77, CI 1.36–2.29) and hypoglycaemia (OR: 2.77, CI 2.22–3.46) Conclusion Clinical and laboratory parameters representing contributors and consequences of severe metabolic acidosis and uraemia were independently associated with these outcomes. The model can be useful for identifying patients at high risk of these complications where laboratory assessments are not routinely available.

scholarly journals Unique prognostic features of grade 3 endometrioid endometrial carcinoma: Findings from 101 consecutive cases at a Japanese tertiary cancer center

2021 ◽  
Vol 60 (2) ◽  
pp. 238-244
Author(s):  
Mayumi Kobayashi Kato ◽  
Hiroshi Yoshida ◽  
Takashi Uehara ◽  
Masaya Uno ◽  
Mitsuya Ishikawa ◽  
...  
Keyword(s):  
Endometrial Carcinoma ◽  
Cancer Center ◽  
Prognostic Features ◽  
Endometrioid Endometrial Carcinoma ◽  
Grade 3 ◽  
Tertiary Cancer Center
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