How should pulmonary nodules be optimally investigated and managed?

Uniportal VATS for Diagnosis and Staging in Non-Small Cell Lung Cancer (NSCLC)

Uniportal VATS has become an accepted approach in minimally invasive thoracic surgery since its first report for lobectomy in 2011. Since the initial restrictions in indications, it has been used in almost all procedures, from conventional lobectomies to sublobar resections, bronchial and vascular sleeve procedures and even tracheal and carinal resections. In addition to its use for treatment, it provides an excellent approach for suspicious solitary undiagnosed nodules after bronchoscopic or transthoracic image-guided biopsy. Uniportal VATS is also used as a surgical staging method in NSCLC due to its low invasiveness in terms of chest tube duration, hospital stay and postoperative pain. In this article, we review the evidence of uniportal VATS accuracy for NSCLC diagnosis and staging and provide technical details and recommendations for its safe performance for that purpose.

Osteosarcoma immune prognostic index can indicate the nature of indeterminate pulmonary nodules and predict the metachronous metastasis in osteosarcoma patients

Purpose

The relationship between indeterminate pulmonary nodules (IPNs) and metastasis is difficult to determine. We expect to explore a predictive model that can assist in indicating the nature of IPNs, as well as predicting the probability of metachronous metastasis in osteosarcoma patients.

Patients and methods

We conducted a retrospective study including 184 osteosarcoma patients at West China Hospital from January 2016 to January 2021. Hematological markers and clinical features of osteosarcoma patients were collected and analyzed.

Results

In this study, we constructed an osteosarcoma immune prognostic index (OIPI) based on the lung immune prognostic index (LIPI). Compared to other hematological markers and clinical features, OIPI had a better ability to predict metastasis. OIPI divided 184 patients into four groups, with the no-OIPI group (34 patients), the light-OIPI group (35 patients), the moderate-OIPI group (75 patients), and the severe-OIPI group (40 patients) (P < 0.0001). Subgroup analysis showed that the OIPI could have a stable predictive effect in both the no-nodule group and the IPN group. Spearman’s rank correlation test and Kruskal–Wallis test demonstrated that the OIPI was related to metastatic site and metastatic time, respectively. In addition, patients with IPNs in high-OIPI (moderate and severe) groups were more likely to develop metastasis than those in low-OIPI (none and light) groups. Furthermore, the combination of OIPI with IPNs can more accurately identify patients with metastasis, in which the high-OIPI group had a higher metastasis rate, and the severe-OIPI group tended to develop metastasis earlier than the no-OIPI group. Finally, we constructed an OIPI-based nomogram to predict 3- and 5-year metastasis rates. This nomogram could bring net benefits for more patients according to the decision curve analysis and clinical impact curve.

Conclusion

This study is the first to assist chest CT in diagnosing the nature of IPNs in osteosarcoma based on hematological markers. Our findings suggested that the OIPI was superior to other hematological markers and that OIPI can act as an auxiliary tool to determine the malignant transformation tendency of IPNs. The combination of OIPI with IPNs can further improve the metastatic predictive ability in osteosarcoma patients.

Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline

CLINICAL QUESTION

The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty.

CURRENT PRACTICE

There have been several guidelines for SPN assessment. Inconsistency of these guidelines makes the clinical application difficult and confusing.

RECOMMENDATIONS

In this Rapid Recommendation, solid and subsolid SPNs are recommended to be evaluated respectively. Six factors, namely the combination of age with sex, smoking history, history of malignancy, family history of malignancy, and nodule size, are recommended for malignancy risk stratification for both kinds of SPNs; the border of nodules (spiculation and lobulation) is recommended for evaluating solid SPNs and the density of nodules (pure or mixed ground-glass nodule) is recommended for subsolid nodules. Among them, smoking history and radiologic features (nodule diameter, border, and density) are of relatively higher importance. A scoring system was proposed to assist malignancy risk stratification of SPNs, with a total score ranging from six points to 15 points (if solid) or 17 points (if subsolid). For each SPN, regardless of solid or subsolid in nature, a total score of ≤ 7 points suggested a low risk of being malignant, while 7 to 9 points suggested medium risk, and ≥ 9 points suggested high risk.

HOW THIS GUIDELINE WAS CREATED

This rapid recommendation was developed using the MAGIC (Making GRADE the Irresistible Choice) methodological framework. First, a clinical subcommittee identified the topic of recommendation and requested evidence. Then, an independent evidence synthesis subcommittee performed a comprehensive literature review and evaluated the evidence. Finally, based on findings from the systematic review and use of real-world data, the clinical subcommittee formulated recommendations, including the scoring system, through a consensus procedure.

THE EVIDENCE

A total of 13857 patients with SPNs were included in the meta-analysis and the association between 12 candidate factors and the risk of SPNs being malignant was studied. Eventually, seven factors were recommended for SPNs evaluation, and a scoring system was proposed.

UNDERSTANDING THE RECOMMENDATION

The parameters included are objective. Therefore, this recommendation is feasible in clinical practice. However, there are several uncertainties, such as a lack of further verification. It might be misclassified by the scoring system. Clinicians could choose the most suitable scheme according to the recommendation, along with their own experience in specific situations.

Decreasing Prevalence of Benign Etiology in Resected Lung Nodules Suspicious for Lung Cancer over the Last Decade

This study investigated treatment strategy for suspicious lung cancer with postoperatively proven benign etiology. In this retrospective study, we collected patients who underwent pulmonary resection for radiologically suspected lung cancer from 2010 to 2019 at Department of Thoracic Surgery, Fudan University Shanghai Cancer Center (FUSCC). Radiological features, preoperative follow-up time, preoperative pathology and postoperative pathology of these patients were documented. We classified resected benign lesions based on paraffin section and compared the therapy management performed on indeterminate lung nodules of 2 time periods (2010-2014 vs 2015-2019). 17,188 patients were included in this cohort and 1,381 (8.03%) cases were postoperatively proved to be benign lesions. Resected benign lesions proportion significantly decreased by years, from 14.5-6.2%. The respective resected benign lesions proportions for pure GGO nodules, part solid nodules and solid nodules were 5.3%, 3.0% and 11.7%. The resected benign lesions rate for patients with longer preoperative follow-up time was much lower (p < 0.001). Among the benign lesions, 14.2% were benign tumors, 25.7% were granulomatous, 30.2% were pneumonia, 18.0% were fibrosis and 11.9% were other types. If we consider that resections for granulomatous and pneumonia radiologically featured as solid nodules exceeding 2 cm, benign tumor and inflammatory pseudotumor are therapeutic, the nontherapeutic pulmonary resection rate is 4.26%. For patients with GGO nodules, the median preoperative follow-up time increased with the time being and the resected benign rate in period 2 (2015-2019) was significantly lower than that in period 1 (2010-2014). Wedge resection was the most common surgery strategy especially for small nodules and no matter for small or large nodules, the frequency of sublobar resection in period 2 was higher than that in period 1. The resected benign lesions rate at our department was relatively low and decreasing over the last decade. Meanwhile, our follow-up and surgical strategy improved over time. For patients with GGO nodules, 4-6months preoperative follow-up is recommended to avoid surgical intervention for benign lesions. For solid nodules with inconclusive diagnosis, limited resection should be first considered to maintain the balance between reducing the risk of cancer progressing and minimizing the resection for benign lesions.

High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules

OBJECTIVE

To investigate whether 3D convolutional neural network (CNN) is able to enhance the classification performance of radiologists in classifying pulmonary non-solid nodules (NSNs).

MATERIALS AND METHODS

Data of patients with solitary NSNs and diagnosed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC) in pathological after surgical resection were analyzed retrospectively. Ultimately, 532 patients in our institution were included in the study: 427 cases (144 AIS, 167 MIA, 116 IAC) were assigned to training dataset and 105 cases (36 AIS, 41 MIA and 28 IAC) were assigned to validation dataset. For external validation, 177 patients (60 AIS, 69 MIA and 48 IAC) from another hospital were assigned to testing dataset. The clinical and morphological characteristics of NSNs were established as radiologists' model. The trained classification model based on 3D CNN was used to identify NSNs types automatically. The evaluation and comparison on classification performance of the two models and CNN + radiologists' model were performed via receiver operating curve (ROC) analysis and integrated discrimination improvement (IDI) index. The Akaike information criterion (AIC) was calculated to find the best-fit model.

RESULTS

In external testing dataset, radiologists' model showed inferior classification performance than CNN model both in discriminating AIS from MIA-IAC and AIS-MIA from IAC (the area under the ROC curve (Az value), 0.693 vs 0.820, P = 0.011; 0.746 vs 0.833, P = 0.026, respectively). However, combining CNN significantly enhanced the classification performance of radiologists and exhibited higher Az values than CNN model alone (Az values, 0.893 vs 0.820, P < 0.001; 0.906 vs 0.833, P < 0.001, respectively). The IDI index further confirmed CNN's contribution to radiologists in classifying NSNs (IDI = 25.8 % (18.3-46.1 %), P < 0.001; IDI = 30.1 % (26.1-45.2 %), P < 0.001, respectively). The CNN + radiologists' model also provided the best fit over radiologists' model and CNN model alone (AIC value 63.3 % vs. 29.5 %, 49.5 %, P < 0.001; 69.2 % vs. 34.9 %, 53.6 %, P < 0.001, respectively).

CONCLUSION

CNN successfully classified NSNs based on CT images and its classification performance were superior to radiologists' model. But the classification performance of radiologists can be significantly enhanced when combined with CNN in classifying NSNs.

Lung Nodules in Melanoma Patients: Morphologic Criteria to Differentiate Non-Metastatic and Metastatic Lesions

Lung nodules are frequent findings in chest computed tomography (CT) in patients with metastatic melanoma. In this study, we assessed the frequency and compared morphologic differences of metastases and benign nodules. We retrospectively evaluated 85 patients with melanoma (AJCC stage III or IV). Inclusion criteria were ≤20 lung nodules and follow-up using CT ≥183 days after baseline. Lung nodules were evaluated for size and morphology. Nodules with significant growth, nodule regression in line with RECIST assessment or histologic confirmation were judged to be metastases. A total of 438 lung nodules were evaluated, of which 68% were metastases. At least one metastasis was found in 78% of patients. A 10 mm diameter cut-off (used for RECIST) showed a specificity of 95% and a sensitivity of 20% for diagnosing metastases. Central location (n = 122) was more common in metastatic nodules (p = 0.009). Subsolid morphology (n = 53) was more frequent (p < 0.001), and calcifications (n = 13) were solely found in non-metastatic lung nodules (p < 0.001). Our data show that lung nodules are prevalent in about two-thirds of melanoma patients (AJCC stage III/IV) and the majority are metastases. Even though we found a few morphologic indicators for metastatic or non-metastatic lung nodules, morphology has limited value to predict the presence of lung metastases.

Protocol and Rationale for the International Lung Screening Trial

Rationale: The NLST (National Lung Screening Trial) reported a 20% reduction in lung cancer mortality with low-dose computed tomography screening; however, important questions on how to optimize screening remain, including which selection criteria are most accurate at detecting lung cancers and what nodule management protocol is most efficient. The PLCO_m2012 (Prostate, Lung, Colorectal and Ovarian) Cancer Screening Trial 6-year and PanCan (Pan-Canadian Early Detection of Lung Cancer) nodule malignancy risk models are two of the better validated risk prediction models for screenee selection and nodule management, respectively. Combined use of these models for participant selection and nodule management could significantly improve screening efficiency.

Objectives: The ILST (International Lung Screening Trial) is a prospective cohort study with two primary aims: 1) Compare the accuracy of the PLCO_m2012 model against U.S. Preventive Services Task Force (USPSTF) criteria for detecting lung cancers and 2) evaluate nodule management efficiency using the PanCan nodule probability calculator-based protocol versus Lung-RADS.

Methods: ILST will recruit 4,500 participants who meet USPSTF and/or PLCO_m2012 risk ≥1.51%/6-year selection criteria. Participants will undergo baseline and 2-year low-dose computed tomography screening. Baseline nodules are managed according to PanCan probability score. Participants will be followed up for a minimum of 5 years. Primary outcomes for aim 1 are the proportion of individuals selected for screening, proportion of lung cancers detected, and positive predictive values of either selection criteria, and outcomes for aim 2 include comparing distributions of individuals and the proportion of lung cancers in each of three management groups: next surveillance scan, early recall scan, or diagnostic evaluation recommended. Statistical powers to detect differences in the four components of primary study aims were ≥82%.

Conclusions: ILST will prospectively evaluate the comparative accuracy and effectiveness of two promising multivariable risk models for screenee selection and nodule management in lung cancer screening.

Clinical trial registered with www.clinicaltrials.gov (NCT02871856).

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study

PURPOSE

To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.e., Filter-Guided Pyramid NETwork (FGP-NET).

MATERIALS AND METHODS

We developed and validated FGP-NET on a collection of 2106 pulmonary nodules on computed tomography images which combined screened and clinically detected nodules, and performed external test (n = 341). The area under the curves (AUCs) of FGP-NET were assessed. A comparison study with a group of 126 skilled radiologists was conducted. On top of FGP-NET, we built up our HONORS which was composed of two solutions. In the Human Free Solution, we used the high sensitivity operating point for screened nodules, but the high specificity operating point for clinically detected nodules. In the Human-Machine Coupling Solution, we used the Youden point.

RESULTS

FGP-NET achieved AUCs of 0.969 and 0.847 for internal and external test. The AUCs of the subsets of the external test set ranged from 0.890 to 0.942. The average sensitivity and specificity of the 126 radiologists were 72.2 ± 15.1 % and 71.7 ± 15.5 %, respectively, while a higher sensitivity (93.3 %) but a relatively inferior specificity (64.0 %) were achieved by FGP-NET. HONORS-guided FGP-NET identified benign nodules with high sensitivity (sensitivity,95.5 %; specificity, 72.5 %) in the screened nodules, and identified malignant nodules with high specificity (sensitivity, 31.0 %; specificity, 97.5 %) in the clinically detected nodules. These nodules could be reliably diagnosed without any intervention from radiologists, via the Human Free Solution. The remaining ambiguous nodules were diagnosed with high performance, which however required manual confirmation by radiologists, via the Human-Machine Coupling Solution.

CONCLUSIONS

FGP-NET performed comparably to skilled radiologists in terms of diagnosing pulmonary nodules. HONORS, due to its high performance, might reliably contribute a second opinion, aiding in optimizing the clinical workflow.

Improvement in Follow-up Imaging With a Patient Tracking System and Computerized Registry for Lung Nodule Management

PURPOSE

Despite established guidelines, radiologists' recommendations and timely follow-up of incidental lung nodules remain variable. To improve follow-up of nodules, a system using standardized language (tracker phrases) recommending time-based follow-up in chest CT reports, coupled with a computerized registry, was created.

MATERIALS AND METHODS

Data were obtained from the electronic health record and a facility-built electronic lung nodule registry. We evaluated two randomly selected patient cohorts with incidental nodules on chest CT reports: before intervention (September 2008 to March 2011) and after intervention (August 2011 to December 2016). Multivariable logistic regression was used to compare the cohorts for the main outcome of timely follow-up, defined as a subsequent report within 13 months of the initial report.

RESULTS

In all, 410 patients were included in the pretracker cohort versus 626 in the tracker cohort. Before system inception, 30% of CT reports lacked an explicit time-based recommendation for nodule follow-up. The proportion of patients with timely follow-up increased from 46% to 55%, and the proportion of those with no documented follow-up or follow-up beyond 24 months decreased from 48% to 31%. The likelihood of timely follow-up increased 41%, adjusted for high risk for lung cancer and age 65 years or older. After system inception, reports missing a tracker phrase for nodule recommendation averaged 6%, without significant interyear variation.

CONCLUSIONS

Standardized language added to CT reports combined with a computerized registry designed to identify and track patients with incidental lung nodules was associated with improved likelihood of follow-up imaging.

Lung Resection Without Tissue Diagnosis: A Pragmatic Perspective on the Indeterminate Pulmonary Nodule

BACKGROUND

The indeterminate pulmonary nodule is a common clinical problem. Preoperative tissue diagnosis is not always possible, despite all attempts. The objectives of this study were to determine the frequency of a malignant diagnosis in this scenario and whether attempted preoperative biopsy impacted estimation of the risk of malignancy.

PATIENTS AND METHODS

We reviewed 500 consecutive cases of pulmonary resection without a preoperative tissue diagnosis at a tertiary care center from 2009 to 2013. Age, sex, smoking status, prior malignancy, tumor size, and whether or not tissue diagnosis had been attempted were recorded. Logistic regression models were constructed to determine factors associated with a malignant diagnosis.

RESULTS

There were 297 males (59.4%), the mean age was 64.9 years, and 412 had a smoking history (82.4%). Also, 203 patients (40.6%) had a malignancy history, and 36 patients (7.2%) had previous lung cancer. Biopsy was attempted for 102 patients (20.5%). The final diagnosis was lung cancer in 336 patients (67.2%), metastatic cancer in 93 patients (18.6%), and benign tumour in 71 patients (14.2%). Male sex, increasing age, smoking history, and prior lung cancer were positive predictors of lung cancer. Model discrimination was good (c-statistic, 0.83). Attempted biopsy did not alter model discrimination.

CONCLUSION

In this cohort, 86% of resected lesions were malignant. The decision to pursue preoperative tissue diagnosis did not change the predictive ability offered by clinical factors. These findings are reassuring in the scenario when a patient is operable but the diagnosis remains unknown.

How Are Indeterminate Pulmonary Nodules at Diagnosis Associated with Survival in Patients with High-Grade Osteosarcoma?

BACKGROUND

Pulmonary metastases are a poor prognostic factor in patients with osteosarcoma; however, the clinical significance of subcentimeter lung nodules and whether they represent a tumor is not fully known. Because the clinician is faced with decisions regarding biopsy, resection, or observation of lung nodules and the potential impact they have on decisions about resection of the primary tumor, this remains an area of uncertainty in patient treatment. Surgical management of the primary tumor is tailored to prognosis, and it is unclear how aggressively patients with indeterminate pulmonary nodules (IPNs), defined as nodules smaller than 1 cm at presentation, should be treated. There is a clear need to better understand the clinical importance of these nodules.

QUESTIONS/PURPOSES

(1) What percentage of patients with high-grade osteosarcoma and spindle cell sarcoma of bone have IPNs at diagnosis? (2) Are IPNs at diagnosis associated with worse metastasis-free and overall survival? (3) Are there any clinical or radiologic factors associated with worse overall survival in patients with IPN?

METHODS

Between 2008 and 2016, 484 patients with a first presentation of osteosarcoma or spindle cell sarcoma of bone were retrospectively identified from an institutional database. Patients with the following were excluded: treatment at another institution (6%, 27 of 484), death related to complications of neoadjuvant chemotherapy (1%, 3 of 484), Grade 1 or 2 on final pathology (4%, 21 of 484) and lack of staging chest CT available for review (0.4%, 2 of 484). All patients with abnormalities on their staging chest CT underwent imaging re-review by a senior radiology consultant and were divided into three groups for comparison: no metastases (70%, 302 of 431), IPN (16%, 68 of 431), and metastases (14%, 61 of 431) at the time of diagnosis. A random subset of CT scans was reviewed by a senior radiology registrar and there was very good agreement between the two reviewers (κ = 0.88). Demographic and oncologic variables as well as treatment details and clinical course were gleaned from a longitudinally maintained institutional database. The three groups did not differ with regard to age, gender, subtype, presence of pathological fracture, tumor site, or chemotherapy-induced necrosis. They differed according to local control strategy and tumor size, with a larger proportion of patients in the metastases group presenting with larger tumor size and undergoing nonoperative treatment. There was no differential loss to follow-up among the three groups. Two percent (6 of 302) of patients with no metastases, no patients with IPN, and 2% (1 of 61) of patients with metastases were lost to follow-up at 1 year postdiagnosis but were not known to have died. Individual treatment decisions were determined as part of a multidisciplinary conference, but in general, patients without obvious metastases received (neo)adjuvant chemotherapy and surgical resection for local control. Patients in the no metastases and IPN groups did not differ in local control strategy. For patients in the IPN group, staging CT images were inspected for IPN characteristics including number, distribution, size, location, presence of mineralization, and shape. Subsequent chest CT images were examined by the same radiologist to reevaluate known nodules for interval change in size and to identify the presence of new nodules. A random subset of chest CT scans were re-reviewed by a senior radiology resident (κ = 0.62). The association of demographic and oncologic variables with metastasis-free and overall survival was first explored using the Kaplan-Meier method (log-rank test) in univariable analyses. All variables that were statistically significant (p < 0.05) in univariable analyses were entered into Cox regression multivariable analyses.

RESULTS

Following re-review of staging chest CTs, IPNs were found in 16% (68 of 431) of patients, while an additional 14% (61 of 431) of patients had lung metastases (parenchymal nodules 10 mm or larger). After controlling for potential confounding variables like local control strategy, tumor size, and chemotherapy-induced necrosis, we found that the presence of an IPN was associated with worse overall survival and a higher incidence of metastases (hazard ratio 1.9 [95% CI 1.3 to 2.8]; p = 0.001 and HR 3.6 [95% CI 2.5 to 5.2]; p < 0.001, respectively). Two-year overall survival for patients with no metastases, IPN, or metastases was 83% [95% CI 78 to 87], 65% [95% CI 52 to 75] and 45% [95% CI 32 to 57], respectively (p = 0.001). In 74% (50 of 68) of patients with IPNs, it became apparent that they were true metastatic lesions at a median of 5.3 months. Eighty-six percent (43 of 50) of these patients had disease progression by 2 years after diagnosis. In multivariable analysis, local control strategy and tumor subtype correlated with overall survival for patients with IPNs. Patients who were treated nonoperatively and who had a secondary sarcoma had worse outcomes (HR 3.6 [95% CI 1.5 to 8.3]; p = 0.003 and HR 3.4 [95% CI 1.1 to 10.0]; p = 0.03). The presence of nodule mineralization was associated with improved overall survival in the univariable analysis (87% [95% CI 39 to 98] versus 57% [95% CI 43 to 69]; p = 0.008), however, because we could not control for other factors in a multivariable analysis, the relationship between mineralization and survival could not be determined. We were unable to detect an association between any other nodule radiologic features and survival.

CONCLUSION

The findings show that the presence of IPNs at diagnosis is associated with poorer survival of affected patients compared with those with normal staging chest CTs. IPNs noted at presentation in patients with high-grade osteosarcoma and spindle cell sarcoma of bone should be discussed with the patient and be considered when making treatment decisions. Further work is required to elucidate how the nodules should be managed.

LEVEL OF EVIDENCE

Level III, prognostic study.

Navigating Diagnostic and Treatment Decisions in Non-Small Cell Lung Cancer: Expert Commentary on the Multidisciplinary Team Approach

Non-small cell lung cancer (NSCLC) accounts for approximately one in five cancer-related deaths, and management requires increasingly complex decision making by health care professionals. Many centers have therefore adopted a multidisciplinary approach to patient care, using the expertise of various specialists to provide the best evidence-based, personalized treatment. However, increasingly complex disease staging, as well as expanded biomarker testing and multimodality management algorithms with novel therapeutics, have driven the need for multifaceted, collaborative decision making to optimally guide the overall treatment process. To keep up with the rapidly evolving treatment landscape, national-level guidelines have been introduced to standardize patient pathways and ensure prompt diagnosis and treatment. Such strategies depend on efficient and effective communication between relevant multidisciplinary team members and have both improved adherence to treatment guidelines and extended patient survival. This article highlights the value of a multidisciplinary approach to diagnosis and staging, treatment decision making, and adverse event management in NSCLC. IMPLICATIONS FOR PRACTICE: This review highlights the value of a multidisciplinary approach to the diagnosis and staging of non-small cell lung cancer (NSCLC) and makes practical suggestions as to how multidisciplinary teams (MDTs) can be best deployed at individual stages of the disease to improve patient outcomes and effectively manage common adverse events. The authors discuss how a collaborative approach, appropriately leveraging the diverse expertise of NSCLC MDT members (including specialist radiation and medical oncologists, chest physicians, pathologists, pulmonologists, surgeons, and nursing staff) can continue to ensure optimal per-patient decision making as treatment options become ever more specialized in the era of biomarker-driven therapeutic strategies.

Indeterminate pulmonary nodules are not associated with worse overall survival in Ewing Sarcoma

AIM

Lung metastases are a negative prognostic factor in Ewing sarcoma, however, the incidence and significance of sub-centimetre pulmonary nodules at diagnosis is unclear. The aims of this study were to (1): determine the incidence of indeterminate pulmonary nodules (IPNs) in patients diagnosed with Ewing sarcoma (2); establish the impact of IPNs on overall and metastasis-free survival and (3) identify patient, oncological and radiological factors that correlate with poorer prognosis in patients that present with IPNs on their staging chest CT.

MATERIALS & METHODS

Between 2008 and 2016, 173 patients with a first presentation of Ewing sarcoma of bone were retrospectively identified from an institutional database. Staging and follow-up chest CTs for all patients with IPN were reviewed by a senior radiologist. Clinical and radiologic course were examined to determine overall- and metastasis-free survival for IPN patients and to identify demographic, oncological or nodule-specific features that predict which IPN represent true lung metastases.

RESULTS

Following radiologic re-review, IPN were found in 8.7% of patients. Overall survival for IPN patients was comparable to those with a normal staging chest CT (2-year overall survival of 73.3% [95% CI 43.6-89] and 89.4% [95% CI 81.6-94], respectively; p = 0.34) and was significantly better than for patients with clear metastases (46.0% [95% CI 31.9-59]; p < 0.0001). Similarly, there was no difference in metastasis-free survival between 'No Metastases' and 'IPN' patients (p = 0.16). Lung metastases developed in 40% of IPN patients at a median 9.6 months. Reduction of nodule size on neoadjuvant chemotherapy was associated with worse overall survival in IPN patients (p = 0.0084).

CONCLUSION

IPN are not uncommon in patients diagnosed with Ewing sarcoma. In this study, we were unable to detect a difference in overall- or metastasis-free survival between patients with IPN at diagnosis and patients with normal staging chest CTs.

The utility of a convolutional neural network (CNN) model score for cancer risk in indeterminate small solid pulmonary nodules, compared to clinical practice according to British Thoracic Society guidelines

PURPOSE

To determine how implementation of an artificial intelligence nodule algorithm, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), at the point of incidental nodule detection would have influenced further investigation and management using a series of threshold scores at both the benign and malignant end of the spectrum.

METHOD

An observational retrospective study was performed in the assessment of nodules between 5-15 mm (158 benign, 32 malignant) detected on CT scans, which were performed as part of routine practice. The LCP-CNN was applied to the baseline CT scan producing a percentage score, and subsequent imaging and management determined for each threshold group. We hypothesized that the 5% low risk threshold group requires only one follow-up, the 0.56% very low risk threshold group requires no follow-up and the 80% high risk threshold group warrants expedited intervention.

RESULTS

The 158 benign nodules had an LCP-CNN score between 0.1 and 70.8%, median 5.5% (IQR 1.4-18.0), whilst the 32 cancer nodules had an LCP-CNN score between 10.1 and 98.7%, median 59.0% (IQR 37.1-83.9). 24/61 CT scans in the 0.56-5% group (n = 37) and 21/21 CT scans <0.56% group (n = 13) could be obviated resulting in an overall reduction of 18.6% (45/242) CT scans in the benign cohort. In the 80% group (n = 10), expedited intervention of malignant nodules could result in a 3.6-month reduction in time delay in 5 cancer patients.

CONCLUSION

We show the potential of artificial intelligence to reduce the need for follow-up scans and intervention in low-scoring benign nodules, whilst potentially accelerating the investigation and treatment of high-scoring cancer nodules.

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the “suspicious nodules” in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the “coarse-to-fine” detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity.

Predicting lung nodules malignancy

BACKGROUND

It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting.

METHODS

The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy.

RESULTS

From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573.

CONCLUSIONS

The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.

Multi-observer concordance and accuracy of the British Thoracic Society scale and other visual assessment qualitative criteria for solid pulmonary nodule assessment using FDG PET-CT

AIM

To compare the interobserver reliability and diagnostic accuracy of the British Thoracic Society (BTS) scale and other visual assessment criteria in the context of 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-computed tomography (CT) evaluation of solid pulmonary nodules (SPNs).

MATERIALS AND METHODS

Fifty patients who underwent FDG PET-CT for assessment of a SPN were identified. Seven reporters with varied experience at four centres graded FDG uptake visually using the British Thoracic Society (BTS) four-point scale. Five reporters also scored SPNs according to three- and five-point visual assessment scales and using semi-quantitative assessment (maximum standardised uptake value [SUV_max]). Interobserver reliability was assessed with the intra-class correlation coefficient (ICC) and weighted Cohen's kappa (κ). Diagnostic performance was evaluated by receiver operator characteristic (ROC) analysis.

RESULTS

Good interobserver reliability was demonstrated with the BTS scale (ICC=0.78, 95% confidence interval [CI]: 0.69-0.85) and five-point scale (ICC=0.78, 95 CI 0.68-0.86), whilst the three-point scale demonstrated moderate reliability (ICC=0.70, 95% CI: 0.59-0.80). Almost perfect agreement was achieved between two consultants (κ=0.85), and substantial agreement between two other consultants (κ=0.78) using the BTS scale. ROC curves for the BTS and five-point scales demonstrated equivalent accuracy (BTS area under the ROC curve [AUC]=0.768; five-point AUC=0.768). SUV_max was no more accurate compared to the BTS scale (SUV_max AUC=0.794; BTS AUC=0.768, p=0.43).

CONCLUSIONS

The BTS scale can be applied reliably by reporters with varied levels of PET-CT reporting experience, across different centres and has a diagnostic performance that is not surpassed by alternative scales.

The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma

Background

Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN.

Methods

This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated.

Results

The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735–0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707–0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723–0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power.

Conclusion

The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.

Computed Tomography-Based Radiomic Features for Diagnosis of Indeterminate Small Pulmonary Nodules

OBJECTIVE

This study aimed to determine the potential of radiomic features extracted from preoperative computed tomography to discriminate malignant from benign indeterminate small (≤10 mm) pulmonary nodules.

METHODS

A total of 197 patients with 210 nodules who underwent surgical resections between January 2011 and March 2017 were analyzed. Three hundred eighty-five radiomic features were extracted from the computed tomographic images. Feature selection and data dimension reduction were performed using the Kruskal-Wallis test, Spearman correlation analysis, and principal component analysis. The random forest was used for radiomic signature building. The receiver operating characteristic curve analysis was used to evaluate the model performance.

RESULTS

Fifteen principal component features were selected for modeling. The area under the curve, sensitivity, specificity, and accuracy of the prediction model were 0.877 (95% confidence interval [CI], 0.795-0.959), 81.8% (95% CI, 72.0%-90.9%), 77.4% (95% CI, 63.9%-89.3%), and 80.0% (95% CI, 72.0%-86.7%) in the validation cohort, respectively.

CONCLUSIONS

Computed tomography-based radiomic features showed good discriminative power for benign and malignant indeterminate small pulmonary nodules.

Management of incidental pulmonary nodules: current strategies and future perspectives

Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.

Transthoracic fine-needle aspiration diagnosis of solid, subsolid, and partially calcified lung nodules: A retrospective study from a single academic center

Background:

The large-scale National Lung Cancer Screening Trial demonstrated an increased detection of early-stage lung cancers using low-dose computed tomography scan in the screening population. It also demonstrated a 20% reduction of lung cancer-related deaths in these patients.

Aims:

Although both solid and subsolid lung nodules are evaluated in studies, subsolid and partially calcified lung nodules are often overlooked.

Materials and Methods:

We reviewed transthoracic fine-needle aspiration (FNA) cases from lung nodule patients in our clinics and correlated cytological diagnoses with radiologic characteristics of lesions. A computer search of the pathology archive was performed over a period of 12 months for transthoracic FNAs, including both CT- and ultrasound-guided biopsies.

Results:

A total of 111 lung nodule cases were identified. Lesions were divided into three categories: solid, subsolid, and partially calcified nodules according to radiographic findings. Of 111 cases, the average sizes of the solid (84 cases), subsolid (22 cases), and calcified (5 cases) lesions were 1.952 ± 2.225, 1.333 ± 1.827, and 1.152 ± 1.984 cm, respectively. The cytological diagnoses of three groups were compared. A diagnosis of malignancy was made in 64.28% (54 cases) in solid, 22.72% (5 cases) in subsolid, and 20% (1 case) in partially calcified nodules. Among benign lesions, eight granulomatous inflammations were identified, including one case of solid, five cases of subsolid, and two cases of calcified nodules.

Conclusions:

Our study indicates that solid nodules have the highest risk of malignancy. Furthermore, the cytological evaluation of subsolid and partially calcified nodules is crucial for the accurate diagnosis and appropriate clinical management of lung nodule patients.

Does aggressive management of solitary pulmonary nodules pay off?

Indeterminate solitary pulmonary nodules (SPNs), measuring up to 3 cm in diameter, are incidental radiological findings. The ever-growing use of modern imaging has increased their detection. The majority of those nodules are benign; however, the possibility of diagnosing early-stage lung cancer still stands. Guidelines for the management of SPNs have never been validated in prospective comparative studies.

Positron emission tomography (PET) is a useful tool to provide functional information on SPNs. However, overall sensitivity and specificity of PET in detecting malignant SPNs of at least 10 mm in diameter are about 90% and false-negative results are reported.

The development of video-assisted thoracic surgery has provided minimally invasive diagnosis and treatment of SPNs. In our series, 105 patients underwent surgery based on combined increased ¹⁸F-labelled 2-fluoro-2-deoxy-d-glucose (FDG) uptake on PET computed tomography and radiological features (morphology and density) without prior histological confirmation. We detected 26 false negatives (24.8%) and only nine false positives (8.57%). Therefore, our minimally invasive surgical approach prevented 25% of patients with lung cancer from a delayed treatment versus only 9% undergoing “overtreatment”.

In our monocentric cohort, patients with SPNs with large diameter, irregular outline, no calcifications, central location, increased FDG uptake and/or subsolid aspect benefited from a primary surgical resection.

There is much debate on the best management of solitary pulmonary nodules. Even if they are mostly benign, they may represent an early-stage lung cancer. Minimally invasive surgical removal is probably the best approach to this insidious disease.http://ow.ly/wMKz30nemjR

Prognostic indicators of pulmonary metastasis in patients with renal cell carcinoma who have undergone radical nephrectomy

The aim of the present study was to validate prognostic indicators of pulmonary metastasis in patients with renal cell carcinoma (RCC) that have undergone nephrectomy treatment. The data from 356 patients who underwent nephrectomy were investigated and subsequently divided into 2 groups, according to the pulmonary metastasis status. The risk factors for pulmonary metastasis were examined in all patients. In the subgroup analysis, the risk factors were additionally verified in patients with pulmonary nodules using univariate and multivariate logistic regression analyses. The status of pulmonary nodules and pulmonary metastasis were confirmed through preoperative chest radiography by two radiologists. Pulmonary metastasis was observed in 33 (9.3%) patients with a median follow-up time of 54.4 months (interquartile range, 38.8-71.8). Patients with pulmonary nodules indicated significantly increased rates of pulmonary metastasis, compared with patients without pulmonary nodules (24.2 vs. 6.1%; P<0.001). In multivariate analysis, the presence of pulmonary nodules [hazard ratio (HR)=3.15; P=0.0262], albumin (HR=0.42; P=0.0490) and pTstage (HR=3.63; P=0.0475) were indicated to be independent prognostic markers for pulmonary metastasis. In subgroup analysis, pTstage was the only independent prognostic indicator for pulmonary metastasis in these patients (HR=9.81; P=0.0033). In patients with RCC, the presence of pulmonary nodules was associated with pulmonary metastasis. Furthermore, pTstage is a negative prognostic indicator in patients with pulmonary nodules. Therefore, a chest radiologic short-term follow-up is required for these patients.

Brock malignancy risk calculator for pulmonary nodules: validation outside a lung cancer screening population

Objective

To assess the performance of the Brock malignancy risk model for pulmonary nodules detected in routine clinical setting.

Methods

In two academic centres in the Netherlands, we established a list of patients aged ≥40 years who received a chest CT scan between 2004 and 2012, resulting in 16 850 and 23 454 eligible subjects. Subsequent diagnosis of lung cancer until the end of 2014 was established through linking with the National Cancer Registry. A nested case–control study was performed (ratio 1:3). Two observers used semiautomated software to annotate the nodules. The Brock model was separately validated on each data set using ROC analysis and compared with a solely size-based model.

Results

After the annotation process the final analysis included 177 malignant and 695 benign nodules for centre A, and 264 malignant and 710 benign nodules for centre B. The full Brock model resulted in areas under the curve (AUCs) of 0.90 and 0.91, while the size-only model yielded significantly lower AUCs of 0.88 and 0.87, respectively (p<0.001). At 10% malignancy risk, the threshold suggested by the British Thoracic Society, sensitivity of the full model was 75% and 81%, specificity was 85% and 84%, positive predictive values were 14% and 10% at negative predictive value (NPV) of 99%. The optimal threshold was 6% for centre A and 8% for centre B, with NPVs >99%.

Discussion

The Brock model shows high predictive discrimination of potentially malignant and benign nodules when validated in an unselected, heterogeneous clinical population. The high NPV may be used to decrease the number of nodule follow-up examinations.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol

INTRODUCTION

Lung cancer is a common cancer, with over 1.3 million cases worldwide each year. Early diagnosis using computed tomography (CT) screening has been shown to reduce mortality but also detect non-malignant nodules that require follow-up scanning or alternative methods of investigation. Practical and accurate tools that can predict the probability that a lung nodule is benign or malignant will help reduce costs and the risk of morbidity and mortality associated with lung cancer.

METHODS

Retrospectively collected data from 1500 patients with pulmonary nodule(s) of up to 15 mm detected on routinely performed CT chest scans aged 18 years old or older from three academic centres in the UK will be used to to develop risk stratification models. Radiological, clinical and patient characteristics will be combined in multivariable logistic regression models to predict nodule malignancy. Data from over 1000 participants recruited in a prospective phase of the study will be used to evaluate model performance. Discrimination, calibration and clinical utility measures will be presented.

Single-stage nonintubated uniportal thoracoscopic resection of synchronous bilateral pulmonary nodules after coil labeling: A case report and literature review

RATIONALE

Preoperative localization of small pulmonary nodules is essential for precise resection, besides, the optimal treatment for pulmonary nodules is controversial and the prognosis without surgery is uncertain.

PATIENT CONCERNS

Herein we present a patient with compromised pulmonary function harboring synchronous triple ground-glass nodules located separately in different pulmonary lobes.

DIAGNOSES

The pathological diagnosis of the nodules were chronic inflammation, inflammatory pseudotumor and atypical adenomatous hyperplasia, respectively.

INTERVENTIONS

The patient underwent single-stage, non-intubated thoracoscopic pulmonary wedge resection after computed tomography-guided coil labeling of the nodules.

OUTCOMES

The postoperative recovery was encouragingly fast without obvious complications.

LESSONS

Non-intubated thoracoscopic pulmonary wedge resection is feasible for patients with compromised lung function, meanwhile, preoperative coil labeling of small nodules is reliable.