研究發現將CTM結合現行的腫瘤指標，例如CEA,CA199，可以增加預測復發的機率。

癌症復發的早期預測仍然很困難，部分原因是篩檢不足且無效生物標記。結合循環腫瘤細胞 (CTC) 和血清 CEA/CA19-9 進行早期預測大腸直腸癌 (CRC) 復發。透過賽爾奈微生醫的稀有細胞成像平台(CytoSCM imaging@cellenvision)。可有效評估生物標記單獨和組合預測CRC復發的預測價值，並進行了初步臨床驗證，可在癌症各個分期發現，其中在晚期病例中觀察到復發。和臨床治療進行了說明。而根據 Kaplan-Meier 分析分組的生物標記對患者進行分層則更為顯著預測 CRC 復發的差異。4 名 CRC 患者在接受首次治療後 3 年內的追蹤中成功預測。研究揭示CTM 在臨床研究和精選生物標記組對預測 CRC 的價值患者在接受治療後早期出現復發，其中 CTM 和血清 CA19-9 可能應用於臨床監測和CRC管理以提高準確性。

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本文開發了一種新的組合指數 P-score，檢測CTC和CTM的數量與動態變化，以不同的加權指數方式，用於在追蹤期間監測和預測肺癌患者的病情狀況。透過賽爾奈微生醫的稀有細胞成像平台(CytoSCM imaging@cellenvision)，我們發現 P 評分的預測能力不僅與傳統癌症標記物，與單一 CTC/CTM 相比，使用 Pscore 可以糾正更多的假陽性，從而提高分析的準確性。根據我們的初步驗證測試，預測和透過 P 評分縱向監測的疾病進展進一步得到了臨床結果數據的證實，其敏感性甚至比單一生物標誌物更好，AUC可以到達96.9%。

Fig. 3. Distribution of the frequency of the detected CTC and CTM in stratified patients, the comparisons of the sensitivity, specificity, and AUC value of CTC/CTM and the P score, and the recurrence rates by P score.

A: Distribution of the frequency of the detected number of CTC and CTM in stratified patients in this study and formulas to calculate CTC factor, CTM factor, the combinational index P score, and the P score-based prediction model. CTC and CTM mean their enumerations respectively.

 A1 is the best AUC values resulted from the best cut-off point of CTC;

A2 is the best AUC values resulted from the best cut-off point of CTM;

B1 is the best cut-off position of CTC;

B2 is the best cut-off position of CTM.

B: Comparison of the sensitivity, specificity, and AUC value of CTC/CTM and the P score in this study.

C: Three ranges of the P score, the corresponding number of cases predicted as PD and recurrence rate of cases stratified by the P score in this study.

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與 CTC 相比， CTM 顯示出可區分出在不同疾病進程的患者中分佈，具有更好的AUC值，可以預測乳癌的病情進展，本研究中，患者在追蹤期間。根據已鑑定的 CTM 分為四種亞型透過考慮不同的組成部分。結合 CEA 和 CA153，計數 CTC 和運用個別病患的 CTM 來監測病情和病患反應在後續的追蹤中接受治療。透過賽爾奈微生醫的稀有細胞成像平台(CytoSCM imaging@cellenvision)，CTM 及其亞型很有用研究癌症轉移和縱向監測癌症的生物標記和有價值的工具，在患者追蹤期間。

Figure 4. Classification of clustered CTC (CTM) by image-based analysis. After immunofluorescent staining to label-specific markers including the PanCK, Hoechst, CD45, and CD3 on CTC/CTM, the cell-loaded SACA-chips were scanned by the automatic imaging platform (CytoSCM imaging system). Digitally labeled images of CTC/CTM in different channels (PanCK: FITC channel; Hoechst33342: UV365 channel; CD45: PE channel, CD3: APC channel, and Merged channel) were further analyzed to detect, enumerate, and identify different CTC subpopulations. (A): Output images of different channels to show the individual CTC identified by the platform; (B): output images of different channels to show four subtypes of clustered CTC identified by the platform, including the CTC cluster with only PanCK+ CTC, the clustered CTC (PanCK+) accompanied by only CD45+ leukocytes in which the ratio of PanCK+ CTC and CD45+ leukocytes was less than 1, the small group of clustered CTC (PanCK+) accompanied by CD45+ leukocytes and CD3+ lymphocytes, in which the ratio of PanCK+ CTC and the sum of CD45+ leukocytes and CD3+ lymphocytes was 1 or more than 1; the large group of clustered CTC (PanCK+) accompanied by CD45+ leukocytes and CD3+ lymphocytes, in which the ratio of PanCK+ CTC and the sum of CD 45+ leukocytes and CD 3+ lymphocytes was less than 1. The red circle in (A) was displayed to show the identified area of the targeted CTC in different channels by the automatic imaging platform. The green circle in (B) was displayed to show the identified CTC in different channels by the automatic imaging platform. The orange circle in (B) was displayed to show the identified leukocytes in different channels by the automatic imaging platform. The red circle in (B) was displayed to show the identified lymphocytes in different channels by the automatic imaging platfo

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