Malignant Pleural Mesothelioma Nodal Status: Where Are We at?

Ricciardi, Sara; Carleo, Francesco; Jaus, Massimo Osvaldo; Martino, Marco; Carbone, Luigi; Rícci, Alberto; Cardillo, Giuseppe

doi:10.3390/jcm10215177

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…A previous study also reported that the prognosis of epithelioid patients with MPM was the best among the 3 subtypes ( 52 ). Positive lymph node status, except that in N2, was also significantly associated with prognosis, and we found that the proportion of patients with MPM lymph node metastasis was nearly 35%, which was roughly the same as that previously reported ( 53 ). However, it remains unclear whether the location and number of lymph nodes are also principal prognostic factors, and further research is needed in this area.…”

Section: Discussionsupporting

confidence: 90%

A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma

Li,

Zhang,

Cai

et al. 2023

Transl Cancer Res

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Background Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. Methods In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM. Results The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model’s performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030–0.7045] in the training cohort and 0.7076 (95% CI: 0.7067–0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy. Conclusions Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.

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Section: Discussionsupporting

confidence: 90%