{"id":1067838,"date":"2024-03-02T02:39:02","date_gmt":"2024-03-02T07:39:02","guid":{"rendered":"https:\/\/www.immortalitymedicine.tv\/exploration-and-machine-learning-model-development-for-t2-nsclc-with-bronchus-infiltration-and-obstructive-nature-com\/"},"modified":"2024-08-18T11:39:51","modified_gmt":"2024-08-18T15:39:51","slug":"exploration-and-machine-learning-model-development-for-t2-nsclc-with-bronchus-infiltration-and-obstructive-nature-com","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/exploration-and-machine-learning-model-development-for-t2-nsclc-with-bronchus-infiltration-and-obstructive-nature-com.php","title":{"rendered":"Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive &#8230; &#8211; Nature.com"},"content":{"rendered":"<p><p>Clinical characteristics of T2 stage NSCLC patients in    different groups    <\/p>\n<p>    Variations in clinical characteristics between the MBI\/(P\/ATL)    and non-MBI\/(P\/ATL) groups were prominently attributed to the    diameter linked to the T2 stage (Table 1). Notable    disparities existed in gender distribution, with the    MBI\/(P\/ATL) group demonstrating a higher proportion of males    (58.4%\/55.3% vs. 53.4%) and a heightened occurrence of Squamous    Cell Carcinoma (46.0%\/40.8% vs. 32.7%). Significantly, a larger    proportion of primary sites in the main bronchus were    identified in the MBI\/(P\/ATL) group (14.1%\/7.8% vs. 1.7%),    accompanied by a more advanced histologic grading    (p<0.001).  <\/p>\n<p>    The MBI\/(P\/ATL) group, especially the P\/ATL subgroup, exhibited    higher incidences of lymph nodes (N0: 41.8%\/34.0% vs. 53.0%).    Regarding treatment modalities, the MBI\/(P\/ATL) group displayed    a stronger propensity to undergo chemotherapy (48.0%\/51.1% vs.    41.7%) and radiation therapy (43.2%\/46.8% vs. 38.2%). Compared    to MBI\/None group, the incidence of surgery was markedly lower    in the P\/ATL subgroup (26.5% vs. 49.9%\/46.1%). Moreover, we    counted those who underwent surgery and found that compared to    surgery alone, the MBI\/(P\/ATL) group experienced a much higher    proportion of preoperative induction therapy or postoperative    adjuvant therapy than the non-MBI\/(P\/ATL) group (41.3%\/54.7%    vs. 36.6%).  <\/p>\n<p>    In relation to tumor diameter, the non-MBI\/(P\/ATL) group had a    larger diameter due to the incorporation of cases surpassing    3cm. In general, profound differences in clinical    characteristics were observed between the groups, with the    MBI\/(P\/ATL) group manifesting extensive disparities, especially    within the P\/ATL subgroup, compared to the non-MBI\/(P\/ATL)    group.  <\/p>\n<p>    Through KaplanMeier survival analysis, it was discerned that    the OS for the MBI (Diameter>3) group was adversely    impacted in comparison to the non-MBI\/(P\/ATL) group (p=0.012)    (Fig.1A). Notably,    regardless of the diameter size, the OS for the non-MBI\/(P\/ATL)    group was significantly superior to that of the P\/ATL group    (p<0.0001) (Fig.1B).  <\/p>\n<p>            KaplanMeier analysis of patients with different T2            types of NSCLC. (A,B) KaplanMeier            analysis of overall survival (OS) in the Pneumonia or            Atelectasis (P\/ATL) and Main Bronchus Infiltration            (MBI) groups versus the groups without P\/ATL and MBI,            prior to propensity score matching (PSM).            (C,D) KaplanMeier analysis of OS in the            P\/ATL and MBI groups versus the non-MBI and P\/ATL            groups following PSM. (E,F) KaplanMeier            analysis of cancer-specific survival (CSS) in the P\/ATL            and MBI groups versus the non-MBI and P\/ATL groups            after PSM.          <\/p>\n<p>    Given the pronounced heterogeneity in clinical characteristics    among the three groups, we adopted the Propensity Score    Matching (PSM) method to mitigate the impact of diverse    background variables, thereby harmonizing potential prognostic    factors between the P\/ATL and MBI groups compared to the    non-MBI\/(P\/ATL) group. This approach ensured that the p-values    from t-tests or chi-square tests for all clinical    characteristics between the respective groups exceeded 0.1,    indicating a balanced comparison (Supplementary data    1). Following this    adjustment, we analyzed OS and cancer-specific survival (CSS)    using the KM method for the P\/ATL vs. None groups and the MBI    vs. None groups, respectively. Our findings revealed that the    P\/ATL group exhibited a significantly poorer prognosis than the    None group, with p of 0.00015 for OS and 0.00021 for CSS    (Fig.1C,E). Conversely, the    MBI group's prognosis was marginally inferior compared to the    None group, with p of 0.037 for OS and 0.016 for CSS    (Fig.1D,F).  <\/p>\n<p>    Our findings indicate that at the T2 stage, both the MBI and    P\/ATL groups demonstrate an elevated risk for lymph node    metastasis. To ascertain whether MBI and P\/ATL act as    independent risk factors for these lymph node metastase, we    employed a multifactorial logistic regression analysis. The    results illuminated those individuals in the MBI\/(P\/ATL) group    had a notably higher risk of lymph node metastasis compared to    those in the non-MBI\/(P\/ATL) group. In detail, MBI was found to    be an independent risk factor for lymph node metastasis    (OR=1.69, 95% CI 1.551.85, p<0.001), as was P\/ATL    (OR=2.10, 95% CI 1.932.28, p<0.001) (Table    2).  <\/p>\n<p>    To evaluate the optimal treatment for NSCLC patients with two    specific types of T2 tumors, we integrated seven treatment    modalities: None, Radiation Therapy Alone, Chemotherapy Alone,    Radiation+Chemotherapy, Surgery Alone, Initial Surgery    Followed by Adjuvant Treatment, and Induction Therapy Followed    by Surgery. We conducted a multifactorial Cox regression    analysis of OS to assess the prognostic impact of these    treatments in patients with P\/ATL and MBI, respectively, using    Surgery Alone as the reference group (Table    3). The results    indicated that surgical treatments significantly outperformed    both Radiotherapy Alone and Chemotherapy Alone, as well as the    combination of Radiotherapy and Chemotherapy, in both    subgroups. Specifically, in patients with MBI, Initial Surgery    Followed by Adjuvant Treatment (HR=0.77, 95% CI 0.670.90,    p=0.001) and Induction Therapy Followed by Surgery    (HR=0.65, 95% CI 0.480.87, p=0.003) were significantly    more effective than Surgery Alone. Conversely, for patients    with P\/ATL, neither Initial Surgery Followed by Adjuvant    Treatment (HR=1.17, 95% CI 0.991.37, p=0.067) nor    Induction Therapy Followed by Surgery (HR=1.05, 95% CI    0.781.40, p=0.758) showed any advantage over Surgery Alone.  <\/p>\n<p>    Given the limited therapeutic options for patients with distant    metastases, we analyzed the KM survival with different    therapeutic strategies for patients with P\/ATL and MBI at    stages N0-1M0 and N2-3M0, respectively. In patients with MBI at    the N2-3M0 stage, preoperative Induction Therapy significantly    improved prognosis, illustrating a marked enhancement in    outcomes. For the N0-1M0 stage in MBI patients, while there was    a clear improvement in median survival with preoperative    Induction Therapy, this improvement did not reach statistical    significance. Additionally, postoperative Adjuvant Therapy    substantially improved outcomes over Surgery Alone for MBI    patients across both N0-1M0 and N2-3M0 stages    (Fig.2A,B). Conversely,    these treatments did not yield significant benefits for    patients with P\/ATL (Fig.2C,D). Moreover, in    both subgroups for the N0-1M0 stage, prognosis following    Surgery Alone was significantly better than with    Chemoradiotherapy, whereas at the N2-3M0 stage, Surgery Alone    did not show superiority over Chemoradiotherapy in terms of    prognosis (Fig.2).  <\/p>\n<p>            KaplanMeier analysis comparing the effectiveness of            various treatment modalities in patients with Main            Bronchus Infiltration (MBI) or Pneumonia\/Atelectasis            (P\/ATL) based on nodal involvement. (A) Overall            Survival (OS) associated with different treatment            approaches in MBI patients classified as N0-1M0.            (B) OS associated with different treatment            approaches in MBI patients classified as N2-3M0.            (C) OS associated with different treatment            approaches in P\/ATL patients classified as N0-1M0.            (D) OS associated with different treatment            approaches in P\/ATL patients classified as N2-3M0.          <\/p>\n<p>    Given the potential notable disparities in clinicopathologic    variables and prognoses across the MBI and P\/ATL subgroups, we    aimed to delve deeper into the varying impacts that different    factors might exhibit on mortality within these subgroups.    Accordingly, multifactorial logistic regression was applied to    analyze the 5-year OS rate within the MBI and P\/ATL subgroups.    In the MBI group, sex, histologic type, grade, age, N stage, M    stage, site, marital status and treatment type were identified    as independent factors associated with 5-year OS. In the P\/ATL    group, histologic type, grade, age, race, N stage, M stage and    treatment type were recognized as independent factors    associated with 5-year OS (Supplementary data 2).  <\/p>\n<p>    We incorporated the factors independently correlated with    5-year OS from the MBI and P\/ATL groups for prognostic    modeling. The patients were randomized into training and test    data groups at a 7:3 ratio. Subsequently, the best parameters    for each model were adjusted and training was conducted within    the training set to optimize performance. In the validation    set, we performed ROC and DCA analyses of MBI and P\/ATL groups    for all models (Fig.3A,B). The XGBoost    model also demonstrated optimal AUC with 0.814 and 0.853    respectively in both MBI and P\/ATL groups, and the DCA curves    further affirmed that the XGBoost model secures a higher net    benefit compared to other models across varying threshold    ranges (Fig.3C,D). The specific    performance of each model in the test set is shown in    Supplementary Data 3. In addition, we    performed the Delong test and found that the XGBoost model    significantly outperforms the rest of the models in both MBI    and P\/ATL (Supplementary Data 4).  <\/p>\n<p>            Receiver Operating Characteristic Curve (ROC) and            Decision Curve Analysis (DCA) analyses of Main Bronchus            Infiltration (MBI) and Pneumonia\/Atelectasis (P\/ATL)            groups. (A) ROC curves for each model in the MBI            group. (B) ROC curves for each model in the            P\/ATL group. (C) DCA curves for each model in            the MBI group. (D) DCA curves for each model in            the P\/ATL group.          <\/p>\n<p>    Consequently, the calibration curves for the XGBoost model in    both the MBI and P\/ATL groups within the test set were also    plotted, revealing commendable predictive performance of the    model (Fig.4A,B). Additionally, we    scrutinized the importance scores of the variables in both    models (Fig.4C,D).  <\/p>\n<p>            Calibration curves and feature significance plots of            the XGBoost model for Main Bronchus Infiltration (MBI)            and Pneumonia\/Atelectasis (P\/ATL) groups. (A)            Calibration curve of the XGBoost model for the MBI            group. (B) Calibration curve of the XGBoost            model for the P\/ATL group. (C) Feature            significance plot of the XGBoost model for the MBI            group. (D) Feature significance plot of the            XGBoost model for the P\/ATL group.          <\/p>\n<p>    To assist researchers and clinicians in utilizing our    prognostic model, we developed user-friendly web applications    for stage T2 NSCLC MBI and P\/ATL groups    (Fig.5A,B), respectively.    The web interface allows users to input clinical features of    new samples, and the application can then help predict survival    probabilities and survival status based on the patient's    information. And the model can help clinicians to develop    appropriate treatment strategies for this subgroup of patients    by first selecting other parameters of a particular patient and    focusing on the change of their 5-year survival by adjusting    different treatments. For example, a 6574year old male    with T2N3M0 stage lung adenocarcinoma, graded as grade III    located in the upper lobe of a married MBI patient, his 5-year    OS was 19.07% if he received Chemoradiotherapy, 23.83% if he    received only surgery, and 5-year OS if he received Induction    therapy followed by surgery was 35.51%, and 31.28% for those    who received Initial surgery followed by adjuvant treatment.  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>See the original post here:<br \/>\n<a target=\"_blank\" href=\"https:\/\/www.nature.com\/articles\/s41598-024-55507-6\" title=\"Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive ... - Nature.com\" rel=\"noopener\">Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive ... - Nature.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> Clinical characteristics of T2 stage NSCLC patients in different groups Variations in clinical characteristics between the MBI\/(P\/ATL) and non-MBI\/(P\/ATL) groups were prominently attributed to the diameter linked to the T2 stage (Table 1). Notable disparities existed in gender distribution, with the MBI\/(P\/ATL) group demonstrating a higher proportion of males (58.4%\/55.3% vs. 53.4%) and a heightened occurrence of Squamous Cell Carcinoma (46.0%\/40.8% vs <a href=\"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/exploration-and-machine-learning-model-development-for-t2-nsclc-with-bronchus-infiltration-and-obstructive-nature-com.php\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"limit_modified_date":"","last_modified_date":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[1231415],"tags":[],"class_list":["post-1067838","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"modified_by":null,"_links":{"self":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1067838"}],"collection":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/comments?post=1067838"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1067838\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/media?parent=1067838"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/categories?post=1067838"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/tags?post=1067838"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}