A five-factor risk-stratification model for cancer-associated myositis in idiopathic inflammatory myopathies and stage-specific survival analysis: a multicentre Japanese MYKO cohort study.
Patients with idiopathic inflammatory myopathies (IIM) are at increased risk of malignancy, particularly around disease onset, but practical tools for risk stratification of cancer-associated myositis (CAM) remain limited. We aimed to characterise CAM in Japanese patients with IIM and develop a clinically applicable risk-stratification model.
We conducted a multicentre retrospective study using the Japanese MYKO cohort, including patients diagnosed with IIM between 2001 and 2024. CAM was defined as malignancy diagnosed within ±3 years of IIM onset. Baseline demographic, clinical, laboratory, and autoantibody variables were compared between patients with and without CAM. Standardised incidence ratios (SIRs) were calculated using age-, sex-, and calendar year-specific cancer incidence rates from the Japanese general population. Random forest analysis and logistic regression were used to derive a CAM risk-stratification model. Overall survival and stage-specific survival were assessed using Kaplan-Meier analysis.
Among 364 patients with IIM, 42 (11.4%) had CAM. Most CAM cases occurred close to IIM onset, with 30 of 42 cases diagnosed within ±1 year. Compared with the general Japanese population, malignancy risk was increased within ±3 years of IIM onset (SIR 2.54, 95% CI 1.81-3.47) and was highest during the first year after onset (SIR 8.66, 95% CI 5.55-12.88). CAM was associated with older age at onset, male sex, smoking history, family history of malignancy, dermatomyositis subtype, dysphagia, elevated C-reactive protein (CRP) and anti-TIF1γ antibody positivity. Anti-TIF1γ positivity was the strongest predictor in random forest analysis. A five-factor model including anti-TIF1γ antibody positivity, elevated CRP, dermatomyositis subtype, older age at IIM onset, and family history of malignancy showed good discriminative performance (AUC 0.86; sensitivity 92.5%; specificity 65.1%). Five-year survival was lower in patients with CAM than in those without CAM (73.6% vs 94.6%) and was better in patients with early-stage than advanced-stage cancer (100% vs 44.0%).
In this multicentre Japanese IIM cohort, malignancies in patients with CAM were diagnosed mainly around IIM onset. A machine learning-derived five-factor model identified patients at increased risk of CAM. An earlier-stage cancer at diagnosis was associated with better survival, highlighting the clinical importance of timely cancer detection.
We conducted a multicentre retrospective study using the Japanese MYKO cohort, including patients diagnosed with IIM between 2001 and 2024. CAM was defined as malignancy diagnosed within ±3 years of IIM onset. Baseline demographic, clinical, laboratory, and autoantibody variables were compared between patients with and without CAM. Standardised incidence ratios (SIRs) were calculated using age-, sex-, and calendar year-specific cancer incidence rates from the Japanese general population. Random forest analysis and logistic regression were used to derive a CAM risk-stratification model. Overall survival and stage-specific survival were assessed using Kaplan-Meier analysis.
Among 364 patients with IIM, 42 (11.4%) had CAM. Most CAM cases occurred close to IIM onset, with 30 of 42 cases diagnosed within ±1 year. Compared with the general Japanese population, malignancy risk was increased within ±3 years of IIM onset (SIR 2.54, 95% CI 1.81-3.47) and was highest during the first year after onset (SIR 8.66, 95% CI 5.55-12.88). CAM was associated with older age at onset, male sex, smoking history, family history of malignancy, dermatomyositis subtype, dysphagia, elevated C-reactive protein (CRP) and anti-TIF1γ antibody positivity. Anti-TIF1γ positivity was the strongest predictor in random forest analysis. A five-factor model including anti-TIF1γ antibody positivity, elevated CRP, dermatomyositis subtype, older age at IIM onset, and family history of malignancy showed good discriminative performance (AUC 0.86; sensitivity 92.5%; specificity 65.1%). Five-year survival was lower in patients with CAM than in those without CAM (73.6% vs 94.6%) and was better in patients with early-stage than advanced-stage cancer (100% vs 44.0%).
In this multicentre Japanese IIM cohort, malignancies in patients with CAM were diagnosed mainly around IIM onset. A machine learning-derived five-factor model identified patients at increased risk of CAM. An earlier-stage cancer at diagnosis was associated with better survival, highlighting the clinical importance of timely cancer detection.
Authors
Oe Oe, Matsuda Matsuda, Akiyama Akiyama, Yamamoto Yamamoto, Suzuka Suzuka, Nakashima Nakashima, Tsuji Tsuji, Sasai Sasai, Nohda Nohda, Yoshida Yoshida, Nakayama Nakayama, Nakakubo Nakakubo, Ogawa Ogawa, Yoshida Yoshida, Hirobe Hirobe, Aitani Aitani, Koshida Koshida, Miyake Miyake, Takeuchi Takeuchi, Kotani Kotani
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