Rethinking the TNM Classification Regarding Direct Lymph Node Invasion in Pancreatic Ductal Adenocarcinoma

Speichinger, Fiona; Dragomir, Mihnea P.; Schallenberg, Simon; Loch, Florian N.; Degro, Claudius E.; Baukloh, Ann-Kathrin; Hartmann, Lisa; Pozios, Ioannis; Schineis, Christian; Margonis, Georgios Antonios; Lauscher, Johannes C.; Beyer, Katharina; Kamphues, Carsten

doi:10.3390/cancers14010201

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Using the TNM system, the “T” plus a letter or number (0 to 4) is used to show how far up or away from its original starting point the tumor has grown, and in the case of cancers, this means how far into the wall of the stomach. The TNM system's numerical classifications are as follows [ 19 – 21 ]: Tis: tumor “in situ” that is caught very early and has not grown beyond stomach lining T1: tumor has grown through lining and into connective tissue T2: tumor has grown into thick inner muscle T3: tumor has spread through outer lining but not to any nearby organs or tissues T4: tumor has spread into nearby tissues or organs …”

Section: Background Studymentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network’s prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

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Section: Background Studymentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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“…26 Lymph-node invasion is a prognostic factor for many cancers such as CRC 27 and pancreatic cancer. 28 In our study, DQB1*03 was negatively associated with lymph-node invasion in CC. Lee et al 27 reported that an increased retrieval of lymph nodes was linked to better survival in CRC.…”

Section: Discussionsupporting

confidence: 52%

HLA class II polymorphisms as prognostic biomarkers for right and left-sided colon cancer

Attia,

Lagha,

Mezlini

et al. 2024

Int J Biol Markers

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Background Colon cancer (CC) is one of the most common malignancies worldwide. Characterization of new prognostic biomarkers for right-sided CC (RCC) and left-sided CC (LCC) may contribute to improving early detection. An association of human leukocyte antigens class II (HLA-II) with the predisposition to CC was suggested. Aim of the study We evaluated the association of DRB1 and DQB1 with the risk of LCC and RCC. Patients and methods Our study comprised 93 CC patients and 100 healthy controls. Genotyping of HLA class II alleles were performed by the Polymerase Chain Reaction Sequence-Specific Primers (PCR-SSP). Results DRB1*03 was positively associated with CC. In contrast, DRB1*11, DRB1*13, DQB1*03, and DQB1*05 were negatively linked to CC. Haplotype analysis revealed that DRB1*04-DQB1*04 and DRB1*09-DQB1*02 were positive, while DRB1*01-DQB1*05, DRB1*04-DQB1*03, DRB1*07-DQB1*02, DRB1*11-DQB1*03, DRB1*11-DQB1*05, and DRB1*13-DQB1*06 were negatively associated with CC. For sigmoid CC, DRB1*13, DRB1*11, and DQB1*05 were negative, while DRB1*04-DQB1*02, and DRB1*07-DQB1*03 were positively associated. DRB1*03 and DRB1*04-DQB1*04 were positive, while DRB1*11 and DQB1*03 were negatively linked to RCC. According to the LCC, DRB1*07, DRB1*11, DQB1*03, DQB1*05, and DRB1*07-DQB1*02 were negative. In contrast, DRB1*09-DQB1*02 was positively associated with LCC. Stratified analysis revealed that DRB1*11 is associated with higher risk of metastasis in CC and sigmoid CC, and tolerance to treatment in RCC. DQB1*03 was associated with lymph-node invasion in CC. Conclusion DRB1 and DQB1 polymorphisms could be used as future biomarkers for the early detection of subjects at a higher risk of developing RCC and LCC, metastasis in sigmoid CC, and tolerance to treatment in RCC.

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“…They concluded that "direct"-only LNI patients had a similar overall survival to those with node-negative disease. Recently, other authors reported similar results [211][212][213].…”

Section: Lymph Node Metastasissupporting

confidence: 61%

Current Pathology Model of Pancreatic Cancer

Szymoński

Milian-Ciesielska²,

Lipiec

et al. 2022

Cancers

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Pancreatic cancer (PC) is one of the most aggressive and lethal malignant neoplasms, ranking in seventh place in the world in terms of the incidence of death, with overall 5-year survival rates still below 10%. The knowledge about PC pathomechanisms is rapidly expanding. Daily reports reveal new aspects of tumor biology, including its molecular and morphological heterogeneity, explain complicated “cross-talk” that happens between the cancer cells and tumor stroma, or the nature of the PC-associated neural remodeling (PANR). Staying up-to-date is hard and crucial at the same time. In this review, we are focusing on a comprehensive summary of PC aspects that are important in pathologic reporting, impact patients’ outcomes, and bring meaningful information for clinicians. Finally, we show promising new trends in diagnostic technologies that might bring a difference in PC early diagnosis.

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Rethinking the TNM Classification Regarding Direct Lymph Node Invasion in Pancreatic Ductal Adenocarcinoma

Cited by 6 publications

References 23 publications

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

HLA class II polymorphisms as prognostic biomarkers for right and left-sided colon cancer

Current Pathology Model of Pancreatic Cancer

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