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Primary intracranial ependymoma is a challenging tumor to treat despite the availability of multidisciplinary therapeutic modalities, including surgical resection, radiotherapy, and adjuvant chemotherapy. After the completion of initial treatment, when resistant tumor cells recur, salvage therapy needs to be carried out with a more precise strategy. Circulating tumor cells (CTCs) have specifically been detected and validated for patients with primary or recurrent diffused glioma. The CTC drug screening platform can be used to perform a mini-invasive liquid biopsy for potential drug selection. The validation of potential drugs in a patient-derived xenograft (PDX) mouse model based on the same patient can serve as a preclinical testing platform. Here, we present the application of a drug testing model in a six-year-old girl with primary ependymoma on the posterior fossa, type A (EPN-PFA). She suffered from tumor recurrence with intracranial and spinal seeding at 2 years after her first operation and extraneural metastases in the pleura, lung, mediastinum, and distant femoral bone at 4 years after initial treatment. The CTC screening platform results showed that everolimus and entrectinib could be used to decrease CTC viability. The therapeutic efficacy of these two therapeutic agents has also been validated in a PDX mouse model from the same patient, and the results showed that these two therapeutic agents significantly decreased tumor growth. After precise drug screening and the combination of focal radiation on the femoral bone with everolimus chemotherapy, the whole-body bone scan showed significant shrinkage of the metastatic tumor on the right femoral bone. This novel approach can combine liquid biopsy, CTC drug testing platforms, and PDX model validation to achieve precision medicine in rare and challenging tumors with extraneural metastases.

Because it is an accessible and routine image test, medical personnel commonly use a chest X-ray for COVID-19 infections. Artificial intelligence (AI) is now widely applied to improve the precision of routine image tests. Hence, we investigated the clinical merit of the chest X-ray to detect COVID-19 when assisted by AI. We used PubMed, Cochrane Library, MedRxiv, ArXiv, and Embase to search for relevant research published between 1 January 2020 and 30 May 2022. We collected essays that dissected AI-based measures used for patients diagnosed with COVID-19 and excluded research lacking measurements using relevant parameters (i.e., sensitivity, specificity, and area under curve). Two independent researchers summarized the information, and discords were eliminated by consensus. A random effects model was used to calculate the pooled sensitivities and specificities. The sensitivity of the included research studies was enhanced by eliminating research with possible heterogeneity. A summary receiver operating characteristic curve (SROC) was generated to investigate the diagnostic value for detecting COVID-19 patients. Nine studies were recruited in this analysis, including 39,603 subjects. The pooled sensitivity and specificity were estimated as 0.9472 (p = 0.0338, 95% CI 0.9009–0.9959) and 0.9610 (p < 0.0001, 95% CI 0.9428–0.9795), respectively. The area under the SROC was 0.98 (95% CI 0.94–1.00). The heterogeneity of diagnostic odds ratio was presented in the recruited studies (I2 = 36.212, p = 0.129). The AI-assisted chest X-ray scan for COVID-19 detection offered excellent diagnostic potential and broader application.